Forthcoming and Online First Articles

International Journal of Medical Engineering and Informatics

International Journal of Medical Engineering and Informatics (IJMEI)

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International Journal of Medical Engineering and Informatics (94 papers in press)

Regular Issues

  • MSCs-released TGF?1generate CD4+CD25+Foxp3+ expression in T-reg cells of Human SLE PBMC   Order a copy of this article
    by Dewi Masyithah Darlan, Delfitri Munir, Agung Putra, Nelva Karmila Jusuf 
    Abstract: Regulatory T-cell (Treg) defects may cause autoreactivity of both T and B cells leading to autoimmune disease, including in Systemic lupus erythematosus (SLE) disease. Those defects were characterized by decreased expression of CD4, CD25, and FoxP3, thus restoring the Treg expression can reverse autoimmunity into immune tolerance into a normal immune response. Mesenchymal stem cells (MSCs) have immunomodulatory properties to control inflammation milieu, including in SLE inflammation by releasing TGF?1, IL-10, and PGE2, thus MSCs can generate Treg cells. However, the regulation of Treg by MSCs-released TGF?1 in human SLE remains unclear. This study aims to analyze the role of MSCs-released TGF?1 in generating CD4+, CD25+, Foxp3+expression in T-reg cells of human SLE PMBCs. This study used a post-test control group design using the co-culture of PBMCs from SLE patient and human umbilical cord MSCs (hUC-MSC) as the subject. This study was divided into 5 groups; sham, control, and treatment group treated by co-cultured hUC-MSC to PBMCs with ratio 1:1 (T1), 1:25 (T2), and 1:50 (T3) for 72 hours incubation, respectively. The expression of T-reg was assessed by flow cytometry assay, whereas the TGF?1 using Cytometric Bead Array (CBA).This study showed a significant increase in Treg cell expression (P
    Keywords: MSCs; TGF?; CD4+CD25+Foxp3+; T-reg; SLE disease.

  • Effective Utilization of Multi Median Variance-Independent Component Analysis on Medical Image Denoising   Order a copy of this article
    by Arathi Thiruvoth, Rahul Chingamtotatil 
    Abstract: Image denoising is a significant pre-processing technique that plays a vital role in medical image processing. Image denoising is the process of removing noise from an image and is a trade-off between noise removal and preservation of significant image details. This paper encloses a sparse representation based denoising technique called Multi Median Variance-Independent Component Analysis (MMV-ICA). Investigation evident, the incorporation of MMV ICA reveals superior denoising results over contest techniques under various noise attacks and noise level conditions. The proposed denoising algorithm based on sparse and redundant representations over learned dictionaries. The dictionary is trained using the corrupted image, and after that, the dictionary is adapted to achieve sparse signal representations. MMV-ICA algorithm presented in this paper makes use of a patch-based dictionary creation method. The paper presents the results of the MMV-ICA denoising technique, which are found to be in par with the existing sparse based denoising methods.
    Keywords: Image Denoising; Sparse Representation and Multi Median Variance-Independent Component Analysis.

  • Early diagnosis of coronary artery disease by SVM, decision tree algorithms and ensemble methods   Order a copy of this article
    by Marziye Narangifard, Hooman Tahayori, Hamid Reza Ghaedsharaf, Mehrdad Tirandazian 
    Abstract: Heart diseases are one of the main causes of death around the world. The most reliable method for heart disease diagnosis is angiography, which is costly, invasive and has the risk of death. This study applies variations of decision tree (DT), support vector machine (SVM) and voting algorithms to construct a heart disease diagnosis predictive model. We show that integrating medical knowledge and statistical knowledge as well as fine tuning the parameters of the used models lead to more effective heart disease diagnosis models. We use two methods for implementing the proposed model. The obtained results in both methods show that voting algorithm and Random Forest outperform other methods. Moreover, the achieved accuracies show improvements over other existing methods.
    Keywords: data mining; machine learning; decision tree; support vector machine; SVM; voting; random forest; forest PA; heart disease; UCI dataset.
    DOI: 10.1504/IJMEI.2021.10041592
     
  • Design of protective vessel and irrigation system for an organ-on-chip device   Order a copy of this article
    by Esmeralda Zuñiga-Aguilar, O. Ramírez-Fernández, Adeodato-Israel Botello-Arredondo 
    Abstract: New devices have been in development in the biomedical engineering field which allow to mimic several physiological processes at once or individually. The present work introduces a design and computational simulation of the nutrient irrigation system, as well as the rapid prototyping of the protective vessel of an organ-on-chip (OOC) device as a way to manipulate and transport the system easily as a whole while maintaining the proper irrigation conditions in the media. The device was generated with the computer-aided design (CAD) software, SolidWorks and the irrigation of the system was performed with the aid of SolidWorks Flow Simulation module. The components of the presented OOC system were manufactured by 3D printing and by using the stereolithography technique. The results showed the flow velocity fields with values in the rage of 0.1830 m/s in the zone were the OOC is located, which indicates would allow a proper irrigation of nutrients to the cells in the chip. The proposed design of the OOC device as a whole, demonstrated to be an adequate storage and handling system for the OOC, in addition of providing a continuous irrigation of the medium.
    Keywords: organ-on-chip; OOC; protective vessel; computer-aided design; CAD; irrigation system; 3D printing; flow simulation; biomedical device.
    DOI: 10.1504/IJMEI.2021.10034729
     
  • A neural network model for preeclampsia prediction based on risk factors   Order a copy of this article
    by Masoumeh Mirzamoradi, Atefeh Ebrahimi, Ali Ameri, Masoumeh Abaspour, Hamid Mokhtari Torshizi 
    Abstract: This study proposes a risk factor-based neural network model for preeclampsia prediction during the second trimester of pregnancy. A total of 320 women giving birth (160 normal delivery, 160 with preeclampsia) at Mahdieh Gynecology Hospital during 2018-2019, were inquired for 13 risk factors. Data from 85% of the subjects (selected randomly) were employed to train the network, and data from the remaining subjects were used to test the performance of the model. This process was repeated 100 times and the average results were determined. The proposed model achieved an accuracy of 83% in classifying the subjects into normal and preeclampsia classes, based on the risk factors input data, with a sensitivity of 83% and a specificity of 82%.
    Keywords: artificial neural network; ANN; prediction; preeclampsia.
    DOI: 10.1504/IJMEI.2021.10043489
     
  • Developing hybrid fuzzy model for predicting severity of end organ damage of the anatomical zones of the lower extremities   Order a copy of this article
    by Nikolay Aleexevich Korenevskiy, Alexander Vladimirovich Bykov, Riad Taha Al-Kasasbeh, Altyn Amanzholovna Aikeyeva, Sofya Nikolaevna Rodionova, Ilyash Maksim, Ashraf Adel Shaqadan 
    Abstract: Treatment of ischemic disease of the lower extremities is challenged by uncertainty and complex correlations among factors. Also, early detection and treatment have valuable effect on saving patients extremities from amputations caused by chronic obliterating diseases of the arteries of the lower extremities. We develop a hybrid fuzzy logic rules model to classify severity of ischemic lesions of the lower extremities so physicians can choose the prevention and treatment course. We use health indicators including rheological indicators, volumetric blood flow velocity, along anatomical zones of the lower extremities and regional systolic blood pressure. The model gives following classes: mild, moderate, severe and critical severity of ischemic damage. Medical experts judgement is integrated used in developing fuzzy rules. The proposed decision rule classification model exceeds 0.95. This model shows increasing necrobiotic changes in the ischemic extremities which helps preserves organ from amputation.
    Keywords: critical ischemia; lower extremities; vascular blood filling; hemostasis; ischemic injury; fuzzy model.
    DOI: 10.1504/IJMEI.2021.10037196
     
  • COVID-19 detection through convolutional neural networks and chest X-ray images   Order a copy of this article
    by K. Venkata Subbareddy, L. Nirmala Devi 
    Abstract: To break the chain of COVID-19, a powerful and fast screening system is required which identifies the COVID-19 affected cases quickly such that the appropriate measures like Quarantine or treatment can be taken. The traditional Genetics assisted chain reaction test is found to have significant misclassification rate followed by more time consuming. To solve this problem, in this paper we have introduced a new model for COVID-19 detection based on Chest X-Ray (CXR) Images and Convolutional Neural Networks (CNNs). The proposed model is an automatic detection model which considers the CXR image as input and performs an in-depth analysis to discover the COVID-19. The proposed CNN model is a very simple and effective which is composed of five convolutional layers and three pooling layers. Every convolutional layer has different sized filters and different number of filters, which extracts all the possible features from CXR image. Simulation experiments are conducted over a newly constructed dataset based on the publicly available CXR (both COVID-19 and Non-CVOID-19) images. Simulation is done under two phases; 3-class and 2-class and obtained an average accuracy of 92.22% and 94.44% respectively. Thus the average accuracy is measured as 93.33%
    Keywords: COVID-19; deep learning; convolutional neural network; CNN; CXR images; accuracy.
    DOI: 10.1504/IJMEI.2021.10041116
     
  • Melanoma classification by 3D colour-texture feature and neural network with improved computational complexity using PCA   Order a copy of this article
    by Mohd Firoz Warsi, Ruqaiya Khanam, Usha Chauhan, Suraj Kamya 
    Abstract: The most severe kind of skin cancer is malignant melanoma. It can grow anywhere on the body. Its exact cause is still unclear but typically it is caused by ultraviolet exposure from sun or tanning beds. Its detection plays a very significant role because if detected early then it is curable, before the spread has begun. In this paper a computationally improved [using principal component analysis (PCA)] feature extraction method named 3D colour texture feature (CTF) is represented which is well discriminative. For classification of melanoma from dermoscopic images, a comparison of different types of machine-learning classification algorithms is evaluated, out of which back propagation neural network (NN) classifier outperforms all other and produce best results, i.e., accuracy = 98.5%, sensitivity = 99.4%, specificity = 95.0%. Obtained results are even better than benchmarking results of PH2 dataset. Comparisons of results with other similar novel works are also discussed.
    Keywords: melanoma; colour texture feature; CTF; dermoscopic image; neural network classifier; PCA; PH2; skin cancer.
    DOI: 10.1504/IJMEI.2021.10035670
     
  • Supervised classification approach for cervical cancer detection using Pap smear images   Order a copy of this article
    by Pallavi V. Mulmule, Rajendra D. Kanphade 
    Abstract: Cervical cancer is found in women and is the global life threatening problem. Papanicolaou test is the well-known technique used for diagnosing the cancer at the early stage. However, the pathological screening is manual, tedious and time consuming process. Therefore, the proposed method employs adaptive fuzzy k means clustering to segment the cell containing nucleus and cytoplasm from the unwanted background from the pathological Pap smear image. Thereafter, the 40 features are extracted from the segmented images based on the shape, size, intensity, orientation, colour, energy and entropy of nucleus and cytoplasm individually. Finally, supervised classification approach utilising multilayer perceptron with three kernels and support vector machine with five different kernels as the classifiers to predict the cancerous cells. The classifier is trained and tested on benchmark database with 280 Pap smear images. The performance of these two classifiers are evaluated and found that the MLP classifier with hyperbolic tangent activation function outperforms in all the performance criterias as compared to SVM classifier with classification accuracy of 97.14%, sensitivity of 98%, Specificity of 95% and positive predictive value of 98%.
    Keywords: cervical cancer; Pap smear stain; pathological images; radial basis function; RBF; multi-layer perceptron; neural network.
    DOI: 10.1504/IJMEI.2021.10038153
     
  • MRI DENOISING: A SPARSE ICA BASED DICTIONARY LEARNING APPROACH   Order a copy of this article
    by Arathi Thiruvoth, Rahul C 
    Abstract: Image denoising is an important preprocessing technique in medical image analysis. The presence of noise in images can lead to degradation in its quality. Image denoising is the process of removing noise from an image and is basically a tradeoff between noise removal and preservation of significant image details. This paper presents a new sparse processing based denoising algorithm, the MMV-ICA (Multi-Median Variance-Independent Component Analysis) denoising algorithm. The MMV-ICA algorithm has been implemented and applied to medical images and the results are analyzed. Various noises which affect medical images are also considered. The proposed denoising algorithm is based on sparse and redundant representations over learned dictionaries. The dictionary is trained using the corrupted image. Thereafter, the dictionary is adapted to achieve sparse signal representations. MMV-ICA algorithm presented in this paper makes use of a patch based dictionary creation method. The paper presents the results of MMV-ICA denoising technique, which are found to be in par with the existing sparse based denoising methods.
    Keywords: Sparse processing; Dictionary learning; Image Denoising; Independent Component Analysis (ICA.

  • Contact less non-invasive method to identify abnormal tongue area using K-mean and problem identification in COVID-19 scenario   Order a copy of this article
    by Pallavi Pahadiya, Ritu Vijay, Kumod Kumar Gupta, Shivani Saxena, Ritu Tandon 
    Abstract: Due to the spread of COVID-19 all around the world there is a need of automatic system for primary tongue ulcer, cancerous cell detection since, everyone do not go to hospital due to the panic and fear of virus spread. These diseases if avoided may spread soon. So, in such situation there is global need of improvement in disease sensing through remote devices using non-invasive methods. Automatic tongue analysis supports the examiner to identify the problem which can be finally verified using invasive methods. In automated tongue analysis image quality, segmentation and area of affected region plays an important role for disease identification. This paper proposes mobile-based image sensing and sending the image to the examiner, examiner if finds issue in image may guide the user to go for further treatment. For segmentation of abnormal area K-mean clustering is used with varying its parameters.
    Keywords: tongue diagnosis system; TDS; image acquisition; thresholding; segmentation; K-mean clustering; mobile app.
    DOI: 10.1504/IJMEI.2021.10043221
     
  • Real-time electrocardiogram monitoring for heart diseases with secured internet of thing protocol   Order a copy of this article
    by Trupti G. Thite, Daulappa G. Bhalake 
    Abstract: Real-time effective ECG data collecting, transmitting, and monitoring system with feature extraction is a big challenge in biomedical signal processing. The electrocardiogram is a widely used testing system to measure and analyse coronary heart diseases, i.e., cardiovascular diseases (CVDs). Heart rate remote monitoring under the service provided by hospital equipment is the current need to improve technologies. IoT enabled medical device helps efficiently to achieve this. To design such systems energy-efficient communication protocol, data-transfer minimisation, assurance of delivery (security), heterogeneous natures of the environment are necessary considerations. This paper outlines a literature survey of three main important areas; real-time ECG monitoring using wearable sensors, feature extraction and classification method for real-time ECG monitoring, and secured IoT protocol for real-time ECG monitoring.
    Keywords: ECG; cardiovascular disease; CVD; tele-monitoring; real-time; tele-health; security; internet-of-things.
    DOI: 10.1504/IJMEI.2021.10037662
     
  • A new hybrid method for left ventricular analysis in cardiac cine MRI   Order a copy of this article
    by Sarra Dali Youcef, Mahammed Messadi 
    Abstract: The increase of deaths by cardiac arrest each year makes the computer-aided diagnosis a necessity for prognosis and treatment of cardiovascular diseases. The cardiac MRI is an imaging technique commonly used for the exploration of the heart. This technique has emerged as a reference for diagnosis of various cardiovascular diseases. In order to analyze the cardiac function in MRI image, the segmentation phase of the left ventricle (LV) is a necessary step to separate left ventricular region from the back ground. Wherefore, thresholding, region growing and active contour model are combined in our approach to obtain the left ventricle form exactly. Finally, the parameters such as end-diastolic volume (EDV), end-systolic volume (ESV) and ejection fraction (EF) are calculated for the LV function quantification. The whole process is applied to the Heart data base containing 18 patients where the experts manual contour is available. Our results show that our method gives an excellent segmentation of LV and a good correlation between our parameters and those obtained by the experts. We have found a correlation of 97% for EDV, 96% for ESV and 89% for EF. They confirm the accuracy of the proposed method and its eventual in aid of diagnosis.
    Keywords: left ventricle; cardiac cine MR images; segmentation; thresholding; region growing; active contour; characterisation; end-diastolic volume; end systolic volume; ejection fraction.
    DOI: 10.1504/IJMEI.2022.10045609
     
  • Detecting heart ailments by investigating ECG with neural networks   Order a copy of this article
    by B. Prabadevi, N. Deepa, L.B. Krithika, Ravi Raj Gulati, R. Sivakumar 
    Abstract: Heart ailments or cardiovascular disease (CVD) are the diseases that incorporate the blood vessels or heart, which is common among various age groups. Though numerous techniques have been used to classify heart abnormalities, such as classification and regression trees (CART) they are less accurate. Therefore, a technique for early detection of heart ailments with more accuracy is mandatory. A model has been designed and proposed to detect the heart ailments using three-layered neural networks for better accuracy. electrocardiogram (ECG or EKG) is used to identify arrhythmia (irregular heartbeat) accurately, and the UC Irvine (UCI) arrhythmia dataset of ECG reports are used to implement a classification for different types of heart abnormalities.
    Keywords: cardiovascular disease; CVD; electrocardiogram; networks; arrhythmia; classification.
    DOI: 10.1504/IJMEI.2021.10043225
     
  • ANALYZING HOSPITAL FACTORS INFLUENCING INTERHOSPITAL SURGICAL SITE INFECTION RATES   Order a copy of this article
    by Vibha Gujar, Shankar Srinivasan, Dinesh Mital, Frederick Coffman 
    Abstract: The surgical site infection (SSI) prevention enactments in the hospitals require comprehensive infection surveillance and control. Each hospital system in the United States displays its own population demographics, pathogenic profiles and surgical volume based on varying geographical location. Remarkably, to stop current challenges like penalties due to poor quality in care, the methods to detect SSI effects and care quality require updates based on meaningful assessment of rates between the hospitals by adding more features. Therefore, utilizing the risk factors adjustment, this retrospective analysis aimed to analyze SSI patients and compare interhospital at-risk individuals based on hospital-related features. The descriptive and regression analysis for each hospital size (small:<250 beds, medium: up to 450 beds, and large: 450+ beds) demonstrated a significant influence of surgical categories, pathogen, hospital location and teaching status on the SSI rates. It distinctively identified patients with cardiovascular and respiratory surgical procedures more in rural than urban hospitals as the high-risk interhospital distinguishing clusters. Though infection rates conversed the population prevalence, adjusting the sociodemographic and other hospital characteristics for the case-mix, regression helped recognize the at-risk patients broadly. The findings from this study, thus, can help hospital organizations to define more case-mix features to device premature cautioning systems before discharge and better tracking approaches.
    Keywords: Surgical site infection; post-surgical infection; catheterization; hospital size.

  • Cell-Seeded Small Intestinal Submucosa (SIS) as a Synthetic Vascular Graft for Implantation in Dogs   Order a copy of this article
    by Mohsen Ahmadi, Behnam Molavi, Ali Ghiaseddin, Shahram Rabbani, Hosein Ahmadi Tafti, Reza Ghiassi, Abtin Mamdouh, Shapoor Shirani 
    Abstract: We investigated the use of small intestinal submucosa (SIS) as a synthetic vascular graft for implantation in a canine model. Three months after implanting the graft in the thoracic aorta of the dogs, animals were sacrificed and grafts were removed for mechanical testing and cell differentiation analysis. The results showed differentiation of bone marrow cells into endothelial smooth cells and lower levels of vimentin, vascular endothelial growth factor receptor (VEGFR), cellularity, and collagen amount in the graft compared to the aorta. In terms of mechanical properties, the grafts were significantly more rigid than the natural aorta. Finally, while the coexistence of differentiated cell layers containing fibroblast, smooth muscle cells and endothelial cells made the SIS a potentially effective artificial graft in the dog model, lack of enough flexibility of the graft remains a problem relative to clinical use of the grafts.
    Keywords: Intestinal submucosa; Biodegradable scaffold; Cell seeding; Synthetic vascular graft.

  • Performance Evaluation of Optimized SVM for Classification of Brain Tumor   Order a copy of this article
    by Arun Kumar, M.A. Ansari, Alaknanda Ashok 
    Abstract: In today's scenario, machine learning tools are most widely used for the classification of images in the field of medical science. Support Vector Machine(SVM) is one of them most popular and highly used for such classifications. Further, such classifications are highly related to the number of features selected from any medical image. The computation time and the memory required for the successful implementation of any classification tool is directly dependent on the number of features. So, in order to get the more accurate classification results, the features of the medical image must be optimized. The present study mainly aims on the development of an improved classification technique by combining with some optimization approaches. In this study, support vector machine is implemented for the classification of the brain tumor by optimizing the features of the Magnetic resonance imaging (MRI) images using three different optimization approaches namely, Particle swarm optimization, Grey wolf optimization and Firefly Algorithm. The results obtained from this study depict that support vector machine along-with the grey wolf optimization provides more accurate classification of the brain tumor with an accuracy of 96.8%
    Keywords: Magnetic resonance imaging;Classification;Optimization; Brain tumor;Supprot Vector Machine.

  • Impact of COVID-19 on Individuals Mental Health and Preventive Health Behaviors: A Conceptual Framework   Order a copy of this article
    by Rajesh Pai, Naganna Chetty, Sreejith Alathur 
    Abstract: The Corona Virus Disease (COVID-19) is a pandemic that facilitate a confrontation space for scientific and social existence of human frontiers. The rapid spread and mortality rate of COVID-19 and the preventive measures including social distancing and its impact on economy, developed an unprecedented consciousness around the globe. It has created an effect on the mental health of individuals employed across various sectors and is outlined in this study. There is currently an inadequate theoretical model that focuses on the comprehensive understanding of the psychology of preventive behavior during the outbreak of pandemics. In this study, a transnational model is delineated for assessing the adoption of preventive behavioral practices associated with COVID-19 pandemic. It uses the components derived from the theories of situational awareness and health belief model and literatures related to impact of containment strategies on various sectors. The contribution include policy recommendations that can be helpful for the healthcare professionals and government to control the disease spread.
    Keywords: COVID-19; Health Belief Model; Situational Awareness; Mental Health; Preventive Health Practices.

  • Comparative analysis of various supervised machine learning algorithms for the early prediction of type-II diabetes mellitus   Order a copy of this article
    by Shahid Mohammad Ganie, Majid Bashir Malik 
    Abstract: Diabetes is one among the top 10 causes of death. Diabetes mellitus is a fatal disease that poses a unique and significant threat to millions of people over the globe. Despite the absolute truth about the statistical data of diabetes from various sources like the World Health Organization, International Diabetes Federation, American Diabetes Association, etc. there is a positive message that early prediction along with appropriate care, diabetes mellitus can be managed and its complications can also be prevented. Nowadays in healthcare sector, machine learning techniques are gaining immense importance through their analytical classification capabilities. Machine learning paradigms are being exploited by researchers for better prediction of diabetes to save human lives. In this paper, a comparison of different supervised machine learning classifiers based on the performance evaluation of various metrics for the early prediction of type-II diabetes mellitus (T2DM) has been performed. The experimental work has been successfully carried out using six machine learning classification algorithms. Among all classifiers, random forest (RF) performs better for predicting T2DM with an accuracy rate of 93.75%. In addition, ten-fold cross-validation method has been applied to remove the class biasness in the dataset.
    Keywords: type-II diabetes mellitus; T2DM; machine learning; framework; logistic regression; LR; Naïve Bayes; NB; support vector machine; SVM; decision tree; DT; random forest; RF; artificial neural network; ANN.
    DOI: 10.1504/IJMEI.2021.10036078
     
  • Computational fluid dynamic analysis of carotid artery with different plaque shapes   Order a copy of this article
    by Raman Yadav, Sharda Vashisth, Ranjit Verma 
    Abstract: Plaque formation in the carotid artery results in carotid artery disease. Atherosclerotic plaque is mostly found at the branching and bifurcation of the artery. The present work investigates the effect of Wall Shear Stress (WSS) and blood flow through carotid artery under various stenosis shapes. Five plaque shapes are considered i.e. plaque at branching, plaque at bifurcation, cosine plaque, irregular plaque, blood clot in external artery. WSS and velocity of blood through stenosed artery was simulated and analyzed using ANSYS Fluent Computational Fluid Dynamics (CFD). Comparison of the wall shear stress at wall artery showed that the artery having blood clot has maximum WSS followed by plaque at bifurcation, cosine shape, irregular shaped stenosis and plaque at branching. It is found that shapes of stenosis play key role in WSS. As stenosis increases in artery WSS also increases. The velocity of flow across stenosis is highest for artery having plaque at bifurcation followed by plaque at branching, irregular plaque, cosine plaque and artery having clot.
    Keywords: Wall Shear Stress; Atherosclerosis; Bifurcation; Stenosis; Plaque shape; Computational Fluid Dynamics.

  • Segmentation of Retinal Blood Vessel structure using Birnbaum-Saunders (Fatigue Life) Probability Distribution Function   Order a copy of this article
    by K. Susheel Kumar, Nagendra Pratap Singh 
    Abstract: Segmentation of the retinal vessel in an eye is a significant task. Retinal blood vessels contain essential information useful in the computer-based diagnosis of various retinal pathologies, such as diabetes, hypertension, etc. In this paper, a novel approach of Probability Distribution Function of Birnbaum Saunders (Fatigue Life) based on matched filtering methods and imported to improve the segmentation of retinal blood vessels concerning existing matched filter methods. In this paper, the retinal blood vessel segmentation divided into preprocessing matched filter-based proposed method and postprocessing. In the preprocessing stage, improve the retinal image quality a different process is known as Principal Component Analysis (PCA) is used to convert to grayscale, followed by a Contrast Limited Adaptive Histogram Equalization known as CLAHE to enhance the grayscale retinal image. For designing of the Birnbaum Saunders (Fatigue Life) based matched filter, suitable values of the different parameters are chosen based on a complete experimental analysis In postprocessing based on an optimization technique based on entropy and length filter for removing the outer artifacts. The proposed approach tested on retinal images of DRIVE database to measure the performance in term of Average True Positive Rate (ATPR), Average False Positive Rate (AFPR), Average Accuracy, Average Root mean square deviation (RMSD), Avg F1-Score and Receiver operating characteristic (ROC) curve plotted. Average Area under the curve (AUC) calculated. The results of values are obtained ATPR 71.39 %, AFPR 2.67 %, Average Accuracy 94.61 %, Average RMSD 0.0054, Average F1-Score 0.684 and Average AUC for DRIVE Dataset 0.9361 respectively.
    Keywords: Birnbaum-Saunders (Fatigue) Probability Distribution Function; Matched filter; Retinal blood vessel segmentation; Optimal thresholding-based entropy.

  • Comparison of image reconstruction algorithms for finding impurities utilising EIT for clinical application in breast cancer   Order a copy of this article
    by Priya Hankare, Alice N. Cheeran, Prashant Bhopale 
    Abstract: Breast cancer is a common and life threatening disease if not treated in its early stage. Electrical impedance tomography is an imaging technique employed in medical field for analysis and diagnosis purpose for early breast cancer disease detection, which is based on voltage and current or impedance measurements. In this paper, 2-dimensional electrical impedance tomography database is used to study and implement various image reconstruction algorithms. The electrical impedance and diffused optical reconstruction software (EIDORS) of MATLAB toolbox is used to reconstruct images of circular phantom approximating a breast hypothetical model.
    Keywords: electrical impedance tomography; EIT; tumour; phantom; image reconstruction.
    DOI: 10.1504/IJMEI.2021.10040190
     
  • Autism Spectrum Disorder Diagnosis and Machine Learning: A review   Order a copy of this article
    by Chandan Jyoti Kumar, Priti Rekha Das, Anil Hazarika 
    Abstract: Autism spectrum disorder (ASD) with global prevalence estimate of approximately 1%, makes it a major social health concern. To make the diagnostic process of ASD faster, convenient and more accurate the researchers have started to apply a dozen of machine learning techniques. This review considers major publications of last decade to identify various aspects of machine learning research in ASD diagnosis. Findings of diagnostic tools and techniques are highlighted so as to detect significant features for machine learning models. Based on types of data, the article categorizes the diagnostic research in two broad categories: behavioral and neuroimaging. In addition, it explores the various findings of these behavioral and neuroimaging techniques in ASD subjects and makes a detailed analysis of performance of these techniques in combination with different machine learning models for ASD diagnosis. This article highlights key research fields of ASD and discusses potential research direction in the future.
    Keywords: Autism Spectrum Disorder; Machine Learning; Neuroimaging; ASD Datasets.

  • A novel method to study resting-state and functional connectivity in infants using coherence analysis of EEG   Order a copy of this article
    by Hemang Shrivastava 
    Abstract: In this study, our goal was to study functional connectivity in infants using event-related potentials (ERPs) of electroencephalography (EEG). We hypothesised that coherence analysis of the power spectral density of tactile stimuli responses would differentiate preterm from full-term infants. In our knowledge, this is the first study demonstrating differences between resting state and tactile functional connectivity using touch stimuli, in preterm infants. We concluded that tactile brain connectivity in full-term infants is more efficient than preterm infants. No statistically significant differences were found in resting-state connectivity for full-term and preterm infants.
    Keywords: functional connectivity; resting-state connectivity; coherence analysis; electroencephalography; EEG; event-related potential; ERP; infant brain development; somatosensory; connectivity networks; small world networks.
    DOI: 10.1504/IJMEI.2021.10040023
     
  • Curtailing Insomnia in Non-Intrusive hardware less Approach with Machine Learning   Order a copy of this article
    by K.V. Shriram, Sini Raj Pulari, Ragu Raman 
    Abstract: The significant challenges nowadays with the expanded utilization of the cell phone are restlessness and risk to mental health. Rest time is implied for the cerebrum to revive. If the rest designs are disturbed because of a nonstop outer aggravation, it upsets the profound rest. Most of us prefer music as the option to induce sleep and relax. Headphones or earphones are used for the same. It is shrewd to turn off the music after the individual rests, which majority of us don\'t do as we are as of now rested by at that point. This causes damage. Excessive usage of earphones or headphones is one part of it and unnecessary feed to the ears while sleeping shall trigger Noise-Induced Hearing Loss. Here, we propose a framework built with Machine Learning as key. This will guarantee that the music player is halted once the individual using has dozed. This ensures proper rest and forestalls sleep deprivation/ NIHL.
    Keywords: Machine Learning; Insomnia; Sleep loss; Noise-Induced Hearing Loss; Technology for sleep; Hearing Loss;.

  • An affordable, intelligent, and fully functional Smart Ventilator System.   Order a copy of this article
    by Bharath Krishnan, Achuth Karakkat, Rohit Mohan Menon, K.V. Shriram 
    Abstract: Because of the Corona Virus Disease (COVID-19) pandemic scenario that the world is going through right now, there has been a surge in the requirement for emergency life support systems like ventilators. Conventional ventilators used in Intensive Care Unit (ICU)s tend to be bulky and expensive and demand high power consumption and trained experts to operate. The aim of the project is to deliver a solution for the growing demand for portable ventilators and a viable replacement for nurse assisted artificial resuscitation. Mechanical ventilation is the process of supplying scheduled breaths to a patient who lacks the ability to do the Work of Breathing (WOB) himself/herself. The pattern of breathing for every patient is identified using sensor(s) and the required volume of air is supplied by compressing a Bag Valve Mask (BVM) device. A machine learning algorithm learns the pattern of breathing and adjusts the pressure and volume controls specific to every patient. All operations and control mode switching for the device can be done using an Android app, hence making it user friendly.
    Keywords: Covid 19; CoronaVirus; ICU; Ventilator; Smart Ventilator; Breathing issues;.

  • IHDPM: an integrated heart disease prediction model for heart disease prediction   Order a copy of this article
    by Abhilash Pati, Manoranjan Parhi, Binod Kumar Pattanayak 
    Abstract: The prediction of heart disease (HD) helps the physicians in taking accurate decisions towards the improvement of patients health. Hence, machine learning (ML), data mining (DM), and classification techniques play a vital role in understanding and reducing the symptoms related to HDs. In this paper, an integrated heart disease prediction model (IHDPM) has been introduced for HD prediction by considering principal component analysis (PCA) for dimensionality reduction, sequential feature selection (SFS) for feature selection, and random forest (RF) classifier for classifications. Some experiments are performed by considering different evaluative measures on Cleveland Heart Disease Dataset (CHDD) sourced from the UCI-ML repository and Python language thereby concluding that the proposed model outperforms the other six conventional classification techniques. The proposed model will help out the physicians in conducting a diagnosis of the heart patients proficiently and at the same time, it can be applicable in predictions of other chronic diseases like diabetes, cancers, etc.
    Keywords: machine learning; ML; data mining; DM; classification techniques; heart disease prediction.
    DOI: 10.1504/IJMEI.2022.10044903
     
  • Harnessing the power of machine learning for breast anomaly prediction using thermograms   Order a copy of this article
    by Aayesha Hakim, R.N. Awale 
    Abstract: Breast cancer is the most fatal cancer among women globally. Thermography provides an early sign of a developing abnormality based on the temperature changes in breasts. In this work, statistical features extracted from the segmented breast region are used for breast cancer prognosis. Machine learning algorithms like support vector machine (SVM), k-nearest neighbourhood (kNN), naive Bayes and logistic regression without and with principal component analysis (PCA) as a pre-cursor are applied to the extracted data to classify thermograms as malignant or benign. Classification was also performed using tree-based classifiers, namely, decision tree and random forest. This work indicates that thermal imaging is capable of predicting breast pathologies coupled with machine learning algorithms. The PCA-SVM model has the best accuracy, sensitivity, specificity and AUROC of 92.74%, 77.77%, 95.83% and 0.8699 respectively. Among tree-based classifiers, random forest classifier has the best accuracy, sensitivity, specificity and AUROC of 94.4%, 97.5%, 78.72% and 0.97961 respectively with five-fold cross validation. Our study produced competitive results when compared to other studies in the literature.
    Keywords: breast cancer; breast thermography; infrared imaging; thermal imaging; machine learning.
    DOI: 10.1504/IJMEI.2021.10040645
     
  • Classification of ECG arrhythmia using significant wavelet-based input features   Order a copy of this article
    by Shivani Saxena, Ritu Vijay, Pallavi Pahadiya, Kumud Kumar Gupta 
    Abstract: This paper proposes an automated approach to classify ECG arrhythmia using wavelet transform and neural network. Wavelet-based optimal ECG feature sets are prepared followed by regression plots in curve fitting. These feature sets are further used for pattern recognition to distinguish in between normal or abnormal arrhythmia classes using multi-layer perceptron neural network (MLP NN). To evaluate performances of the designed classifier accuracy, selectivity and sensitivity parameters are measured. The average accuracy of the classifier is 99.05% which is comparatively higher than the existing methods with dependence on less input features.
    Keywords: ECG arrhythmia; MLP NN; performance indices; regression plot; wavelet transform.
    DOI: 10.1504/IJMEI.2021.10040410
     
  • Managing infectious and inflammatory complications in closed kidney injuries on the basis of fuzzy models   Order a copy of this article
    by Nikolay Korenevskiy, Seregin Stanislav Petrovich, Riad Taha Al-Kasasbeh, Ayman Ahmad Alqaralleh, Gennadij Vjacheslavovich Siplivyj, Mahdi Salman Alshamasin, Sofia Nikolaevna Rodionova, Ivan Mikhailovich Kholimenko, Maxim Yurievich Ilyash 
    Abstract: The aim of the work is to reduce the time and cost of forecasting, preventing and treating patients with infectious complications with kidney injuries. The studies are based on the results of a retrospective analysis of medical records of 123 patients with various forms of kidney injury. Using the methods of analysis, it was shown that in order to achieve acceptable for practice quality prediction of possible infectious and inflammatory complications. The level of psycho-emotional stress and energy of biologically active points connected to the kidneys is used as additional prognostic signs, confidence in the correct prediction increases to 0.93.
    Keywords: class membership functions; exploratory analysis; fuzzy logic; level of psycho-emotional stress; prognosis; risk of developing infectious and inflammatory complications; treatment regimens.
    DOI: 10.1504/IJMEI.2021.10040614
     
  • Analysis of body constitutions discrimination based on radial pulse wave by SVM   Order a copy of this article
    by Nan Li 
    Abstract: In this work, a new method for distinguishing humans physical constitution based on pulse information is proposed. Firstly, pulse data were collected, processed and pulse cycles were segmented. Secondly, time domain features, features coefficients, power spectrums and energy values of pulse wave were extracted and analysed, respectively. Finally, pulse features were evaluated and classified to distinguish different body constitutions by SVM classifier. The experiment indicated that the features selected could be appropriately used to analyze the physical constitutions and can serve as the basis for research on constitution assessment based on Traditional Chinese Medicine pulse diagnosis.
    Keywords: Traditional Chinese Medicine; Pulse Characteristics; Body Constitution; SVM.

  • Layer-based deep net models for automated classification of pulmonary tuberculosis from chest radiographs   Order a copy of this article
    by Sushil Ghildiyal, Saibal Manna, N. Ruban 
    Abstract: Tuberculosis (TB) is a highly infectious bacterial disease. However, it can affect any body part, but is majorly a lung infection; which is potentially fatal and contagious. Like most of the serious health issues, the recovery rate of a symptomatic TB patient completely depends on the early detection and treatment. Deep learning algorithms based computer aided diagnosis (CAD) system, can provide aid in early detection of the disease. In this regard, a method to detect infection of tuberculosis, which uses deep learning network to classify CXR images as normal or abnormal is presented. Convolutional neural network (CNN), visual geometry group (VGG16) and high-resolution network (HRNet) models are used and their performance has been compared based on the validation loss and validation accuracy. The HRNet provides 89.7% accuracy with comparatively less loss among the proposed algorithms. The models are also deployed in android application for active clinical trials.
    Keywords: tuberculosis; deep neural network; convolutional neural; CNN; VGG16; high-resolution network; HRNet.
    DOI: 10.1504/IJMEI.2021.10043722
     
  • Outbreak Trends of Fatality Rate into Coronavirus Disease - 2019 using Deep Learning   Order a copy of this article
    by Robin Singh Bhadoria, Yash Gupta, Ivan Perl 
    Abstract: The World Health Organization (WHO) has declared the novel coronavirus as global pandemic on 11th March 2020. It is supposed to known from Wuhan, China and its spread is unstoppable due to no proper medication and vaccine. The developed forecasting models to predict the number of cases and its fatality rate for coronavirus disease 2019 (COVID-19) which is highly impulsive. This paper provides an intrinsic algorithms namely Linear Regression and Long short-term memory (LSTM) using deep learning for time series based prediction. It also uses the ReLU activation function and Adam optimizer. This paper also reports a comparative study on existing models for COVID-19 cases from different continents in the world. It also provide an extensive model that a brief the prediction about the number of cases and time for recovered, active and deaths rate till January 2021.
    Keywords: Pandemic Analysis; Coronavirus Disease – 2019 (COVID-19); Linear Regression; Time Series forecasting; Long Short-Term Memory (LSTM); Deep learning;.

  • A Hybrid Random Forest based Feature selection model using Mutual Information and F-score for Preterm birth classification.   Order a copy of this article
    by Himani Deshpande, Leena Ragha 
    Abstract: Every womans body is unique and will have some features playing a vital role contributing towards a healthy pregnancy and manually it is difficult to decide the important features to be observed to prevent pregnancy complications. In this proposal, we have considered 21 physical features of 903 women of varied age groups, economic status and health conditions. Variation and Information based Random Forest(VIBRF) hybrid model using mutual information and F-score is applied to evaluate each feature looking into the variation within the feature and mutual information across the features. We experimented using various classifiers, and it is observed that Gaussian NB has shown the most significant improvement in terms of prediction accuracy, from 31% with all features to 80% with our feature selection process. Though SVM prediction accuracy is 84% it is observed AUC drastically improved for GNB by 10%. As it is a medical application, it is important to achieve higher AUC and so through this experiment, it is concluded that GNB performs better with the proposed model.
    Keywords: Features Selection; F-score; Decision Tree; Random Forest; Hybrid Model; Preterm birth; Classification.

  • Perception and confusion of speech in Algerian school children wearing hearing aids   Order a copy of this article
    by Kamel Ferrat, Samir Benyahia 
    Abstract: The paper discusses the performance of hearing impaired Algerian children in perception of features of the Arabic phonemes in comparison to their counterparts with normal hearing ability. The acoustic analysis of collected data demonstrates a presence of several articulation disorders at primary school children with average deafness and wearers of hearing aids, such as: the substitution, elision, assimilation, addition and deformation of Arabic consonants in continuous speech. The consonants prone to these disorders are the back consonants mainly the fricatives and sibilants. Therefore, school rehabilitation services should take into account these constraints to achieve better schooling of hearing impaired schoolchildren.
    Keywords: average deafness; acoustic analysis; hearing aids; primary school; Arabic language.
    DOI: 10.1504/IJMEI.2021.10042045
     
  • Design and Development of IoT - WBAN based Biomedical Solutions via Three - Tier Approach   Order a copy of this article
    by Sonal , S.R.N. Reddy, Dinesh Kumar 
    Abstract: This paper discusses a multi-sensor network based on the IoT-WBAN architecture, designed and developed using a threetier approach to simplify the configuration and networking of sensor nodes. The proposed framework has been developed to take into account various real-time criteria, such as affordable, unobtrusive, non-invasive monitoring at anytime and anywhere. The device is made up of multiple wireless sensor nodes, each recording the various physiological parameters of the patient. At the hub/aggregating unit at TIER-1, the separate data obtained are aggregated and then sent to the base station (TIER-2) for remote transmission (TIER-3). The base station functions as an intermediary point for the transmission of long-range data over the internet or cell network. In order to address the current constraints, numerous design challenges have been considered.
    Keywords: Sensor Node Designing; IoT; WBAN; CHD.

  • Class prediction of the prevalent transmission mode of COVID-19 within a geographic area   Order a copy of this article
    by Donald Douglas Atsa'am, Ruth Wario 
    Abstract: This research developed a multinomial classification model that predicts the prevalent mode of transmission of the coronavirus from person to person within a geographic area, using data from the World Health Organization (WHO). The WHO defines four transmission modes of the coronavirus disease 2019 (COVID-19); namely, community transmission, pending (unknown), sporadic cases, and clusters of cases. The logistic regression was deployed on the COVID-19 dataset to construct a multinomial model that can predict the prevalent transmission mode of coronavirus within a geographic area. The k-fold cross validation was employed to test predictive accuracy of the model, which yielded 73% accuracy. This model can be adopted by local authorities such as regional, state, local government, and cities, to predict the prevalent transmission mode of the virus within their territories. The outcome of the prediction will determine the appropriate strategies to put in place or re-enforced to curtail further transmission.
    Keywords: COVID-19; transmission mode; multi-class prediction; predictive model; community transmission.
    DOI: 10.1504/IJMEI.2021.10038841
     
  • A Comprehensive Review on the Diagnosis and Testing Strategies for Coronavirus Disease (COVID-19).   Order a copy of this article
    by Ragul V, Vishnu Priya Veeraraghavan, KRISHNA MOHAN SURAPANENI, Shanmugarathinam A, Niyas Ahamed 
    Abstract: The COVID-19 outbreak has fashioned to severe threat to each and every individual in social and economic aspects in the country. This can be ascribed to the unreliable properties of COVID-19: it poses a unique standard of broadcast and death ratios. From this review, the probable property of these deadly transmissible viruses is related to that of SARS-CoV-2 as a fright zone of viruses. These agents can be effective and accurate identification which is target separation and management in the affected individuals are essential at the initial phase of viral attack. This is a very crucial and wide factor to know how it is different and dominant, to determine effective vaccines to avoid the transmission of these deadly causative agents. As of September 2020, more than 100 diagnostic kits and developing technologies were used for the detection of COVID-19 are surveyed in this review. The effective management and control of PZV and SARS-CoV-2 are more important to reduce the pandemic situation. The specified and accurate diagnostic and sensitivity materials are required to detect the above wide threat virus in the society by using nucleic acid based diagnosis which help to decrease the negative results. At last the drastic effect of this virus required dynamic defense in the entire world, The society entirely dependent on both private NGOs and government sections for the development of cost effective and constant testing kits in general PZV in future.
    Keywords: COVID-19; SARS-Co-2; Panic Zone of Virus; Pandemic.

  • Automatic detection of Novel Corona virus (SARS-CoV-2) infection in computed tomography scan based on local adaptive thresholding and kernel-support vectors   Order a copy of this article
    by Ritam Sharma, Jankiballabh Sharma, Ranjan Maheshwari 
    Abstract: The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.
    Keywords: COVID-19; CT; artificial Intelligence; textural feature; adaptive thresholding; support vector machine.

  • Early Detection of Parkinsons Disease by using Neuroimaging and biomarkers through Hard and Soft classifiers   Order a copy of this article
    by Gunjan Pahuja, Bhanu Prasad 
    Abstract: Early and accurate detection of Parkinson's disease (PD) remains a challenge. Two prevalent approaches used for the detection of PD are: (i) Dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 123I-Ioflupane and (ii) Cerebrospinal Fluid (CSF) biomarkers. Striatal Binding Ratio (SBR) values are computed from SPECT and, in this research, it is found that if these SBR values are complemented with CSF biomarkers then these SBR values help increase the accuracy of early PD detection. In this study, SBR values for each of the four striatal regions are complemented with some CSF biomarkers to develop a model for the classification and prediction of early PD. A hard classifier is used for developing the classification submodel, and a soft classifier is used for developing the prediction submodel. The results indicate the effectiveness of the developed model.
    Keywords: Parkinson’s Disease (PD); Striatal Binding Ratio (SBR); hard classifier; soft classifier; Multivariate Logistic Regression (MLR); risk prediction; biomarkers.

  • Computer vision-based approach for detecting arm-flapping as autism suspect behaviour   Order a copy of this article
    by Esraa T. Sadek, Noha A. Seada, Said Ghoniemy 
    Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental condition that is characterised by frequent and restrictive motor activities, besides social and communicative disorders. It is considered one of the most rapidly evolving neurodevelopmental disorders in children recently. Repetitive motor behaviours, like arm-flapping and head rocking, may lead to attention distraction and self-injury in severe cases. In this research, a computer-vision-based neural network framework is proposed to automatically detect significant arm-flapping behaviour in autistics. The proposed framework goes through four main phases which are data pre-processing, pose estimation and skeleton representation, data post-processing, and action classification. The proposed framework was tested on three datasets and proved its applicability in real-world applications. The attained accuracy was better compared to that of the state-of-the-art methods. The proposed solution can be used to assist clinicians, and parents to automatically detect this behaviour to offer the child the appropriate medical care once a behavioural abnormality is detected.
    Keywords: autism spectrum disorder; ASD; arm flapping; computer vision; neural networks.
    DOI: 10.1504/IJMEI.2022.10044009
     
  • PATIENT RELATED RISKS AND OUTCOME ASSESSMENTS ON INTERHOSPITAL SURGICAL SITE INFECTION RATES   Order a copy of this article
    by Vibha Gujar, Shankar Srinivasan, Dinesh Mital, Frederick Coffman 
    Abstract: This paper investigates the effect of hospital size (sizes: small:<250 beds, medium: up to 450 beds, and large: 450+ beds) and surgical site infection (SSI) on patient-related risks and treatment outcomes using multi-level hierarchy method. We employed the National Inpatient Sample (NIS) data, available through the Healthcare Cost and Utilization Project (HCUP-NIS, the year 2008 2012) as secondary data with an updated set of case definitions. The SSI risks and outcomes were assessed using regression and propensity score matching analysis. Out of the total 222,845 SSI patients, unadjusted prevalence rates (per 100 procedures) were 2.9% in small, 2.62% in medium, and 2.65% in large hospitals (p<0.0001). Small hospitals at rural locations charged SSI patients up to 33.4% (average: $30,891), about 12% more than urban hospitals within the first 15-30 days of hospital stay. However, results indicate that large hospitals were significantly efficient in infection prevention and had a lesser cost burden than small hospitals. The association of SSI and its impact on outcomes were relatively uniform, but the consequence appeared to be mediated by age, disease severity, neighborhood or economic conditions, elective, and transferred admissions for specific hospital size. Surveillance and policy targeting at refining the efficiency of infection prevention should thus focus on smaller hospitals.
    Keywords: Surgical site infection; hospital size; secondary data; propensity score; case definition; total charges.

  • Application of chaos theory for arrhythmia detection in pathological databases   Order a copy of this article
    by VARUN GUPTA 
    Abstract: To handle the current pathological situation of heart-related diseases, various techniques belonging to automatic Electro-Cardio-Gram (ECG) signal analysis are already available but have not succeeded. In this paper, Savitzky-Golay Filtering (SGF) and Support Vector Machine (SVM) techniques are used for preprocessing and classification purposes. Feature extraction algorithms play a vital role in biomedical signal processing (BSP). For that purpose, the chaos analysis theory is used as a feature extraction tool on different pathological datasets obtained from different cardiology labs to classify different arrhythmia types. The effectiveness of the proposed methodology is evaluated on different performance evaluating parameters viz. sensitivity (Se), accuracy (Acc), and duplicity (D). The proposed methodology presented Se of 99.87%, Acc of 99.72%, and D of 0.066%.
    Keywords: Electro-Cardio-Gram (ECG) signal; Heart-related diseases; Biomedical signal processing (BSP); Chaos analysis.

  • Adaptive Neuro-Fuzzy Inference System for the diagnosis of non-mechanical low back pain   Order a copy of this article
    by Mehrdad Farzandipour, Ehsan Nabovati, Esmaeil Fakharian, Hossein Akbari, Soheila Saeedi 
    Abstract: Back pain is one of the most important causes of disability. Clinical Decision Support Systems (CDSSs) can help physicians diagnose diseases with greater precision. This study designs and implements a CDSS to diagnose non-mechanical Low Back Pain (LBP), including spinal brucellosis, ankylosing spondylitis, spinal tuberculosis, and spinal osteoarthritis using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The highest corrected classification percentage was related to Spinal brucellosis (82.8%), and CDSS was able to differentiate four non-mechanical LBP types.
    Keywords: Clinical Decision Support System; Non-Mechanical Low Back Pain; Adaptive Neuro-Fuzzy Inference System; Diagnose.

  • ICG Signal Noise Cancellation Algorithms for Non-invasive Hemodynamic Monitoring   Order a copy of this article
    by Hadjer BENABDALLAH, Salim KERAI 
    Abstract: Impedance Cardiography (ICG) non-invasive technique is a new way in the medical field, used for diagnosis and monitoring of cardiovascular disorders. The signal processing domain developed several denoising techniques applied for the cancellation of respiratory and movements artifacts that corrupted the acquired signal shape. For this main purpose, our paper-based on a comparative study between different type of adaptive filters and Savitzky-Golay (SG) filtering, those are applied to the sensed impedance to noise removal for hemodynamic parameters monitoring. The results demonstrated in this work are more suitable; it confirmed that the proposed SG denoising filter outperformed the other techniques cited in state of art in terms of preserving information carried over on the signal when the mean error rate of the SG technique is equal to 0.00038 %.
    Keywords: Impedance Cardiography; Adaptive Filters; Savitzky-Golay; Mean Error Rate.

  • Improving prediction of one-year mortality of acute myocardial infarction using machine learning techniques   Order a copy of this article
    by Mirza Touseef, Najla Raza, Adeel Zafar, Muhammad Zubair, Saad Zafar 
    Abstract: The purpose of our study was to improve the prediction of one-year mortality for patients with acute myocardial infarction (AMI). We implemented and compared four classical machine learning algorithms and one deep neural network algorithm. For evaluation metrics, we used accuracy, F1-measure, precision, recall, and area under the receiver operating characteristic curve (AUC). Random forest achieved the best performance based on an AUC of 0.98 with an accuracy of 92%. Results show that our model can predict one-year mortality of AMI with an improved AUC and accuracy using a minimum number of features as compared to previous related studies.
    Keywords: machine learning; deep neural networks; acute myocardial infarction; AMI; mortality prediction; cardiovascular diseases; CVDs.
    DOI: 10.1504/IJMEI.2021.10041072
     
  • Estimation Techniques of Vocal Fold Disorder: A Survey   Order a copy of this article
    by Satyajit Pangaonkar, Reena Gunjan 
    Abstract: Voice disorder of the speech has been observed as an utmost critical issue in both children and elders. Complexity, limited experimentation and predictions by the physicians during the analysis are main causes for inaccurate diagnosis and wrong information about the dysfunction. The research studies are limited to elementary analysis and diagnosis level for the physicians & therapists due the restrictions of skilled specialists and the expenses involved. This paper deals with the evaluation and facts to understand software tools, different acoustic, non-acoustic and non-invasive approach. This can be observed in terms of sensors and image extraction analysis to deal with the pathologies and to overcome the problems which will be beneficial to the speech language pathologists, clinicians, therapist and vocalist.
    Keywords: FonaDyn; PRAAT; MDVP; TERC; PVDF; P-MIC; HSV; VKG.

  • A new Parkinson detection system based on evolutionary fast learning networks and voice measurements   Order a copy of this article
    by Ayoub Bouslah, Nora Taleb 
    Abstract: Parkinsons disease (PD) is become the second most neurological syndrome of the central nervous system after Alzheimers disease. It causes diverse symptoms which include bradykinesia (slowness of movement), voice impairments, rigidity, tremor, and poor balance. The PD recognition system based on voice is founded a noninvasive alternative, but involves rather complex measurements or variables. Therefore an attention is required toward new approaches for better forecasting accuracy. In this paper, an optimal Fast Learning Network (FLN) based on genetic algorithm (GA) was established as PD Diagnosis system. FLN is a double parallel feed-forward neural network structure, as a matter of fact, GA for features reduction and hyperparameters optimization of the FLN which is used as a predictive model. Finally, the conducted experiments on the Parkinson data of voice recordings over Ten Fold Cross-Validation show that proposed system is less complex and also achieved better average classification results with an accuracy of 97.47 %. At the same time, its effective in automatic identification of important vocal features. Moreover, the highest average degree of improved accuracy was (2.1 %) compared with other familiar wrappers including Support Vector Machine and K-nearest Neigbors in the similar conditions.
    Keywords: Parkinson’s disease (PD); Fast Learning Network(FLN); genetic algorithm (GA); features reduction; hyperparameters optimization; predictive model; PD Diagnosis system.

  • Classification of Electroencephalography Signals using Three-Dimensions Convolution Neural Network with Long Short Term Memory Architecture   Order a copy of this article
    by Viet Quoc Huynh, Hoang-Thuy-Tien Vo, Thu Anh Nguyen, Tuan Van Huynh 
    Abstract: This research attempts to use the electroencephalography signals based on physiological signals extracted from the database for emotion analysis to classify the emotion of the subjects by classifier neural network algorithms. In this work, two types of neural network including 3D convolution neural network and hybrid network (3D convolution neural network model combined with long short term memory architecture) were applied to train and test its ability of emotion states classification. As a result, the hybrid network gave the most efficient classification with an accuracy of around 80\%, which was better than other algorithms such as Support Vector Machine, Random Forest, Convolution Neural Network. Furthermore, the results also showed that the accuracy achieved differently at various frequency bands, in which Delta frequency band gave the highest accuracy. Combining signals of different frequencies helped to improve the classification efficiency.
    Keywords: Electroencephalography; Convolution neural network; LSTM; Hybrid network.

  • Current status of Hydroxychloroquine and Azithromycin for the Treatment of COVID-19: An Observational Review   Order a copy of this article
    by PRATEEK PRATYASHA, ADITYA PRASAD PADHY 
    Abstract: Though effect coronavirus has known to be a catastrophic pandemic since 100 of years, Severe Acute Respiratory Syndrome-2 coronavirus (SARS2-CoV) was first claimed to be emerged in December 2019 at the city of Wuhan, China. Abruptly, the virus dominated more than 218 countries with 157,566,607 confirmed cases and the death figure has reached nearly 32,84,551 till time. Recently the pandemic is getting worse day by day, people are suffering from hypoxia and server respiratory problem despite of the unstoppable service of healthcare sector. Prior concern behind this emergency is that, till date researchers and scientists were failed to invent any productive pharmaceutical treatment to weed out the infection completely. Although, vaccination is publically available; but it is applicable only for precaution purpose and not evident for preventive measures. This review focuses on therauptiuc status to control the severity of SAS2-CoV agent. The approach aims at implicating a low toxic metabolite anti-malarial drug, Hydroxychloroquine combined with an antibiotic called Azithromycin for the treatment of acute respiratory disturbance and hypoxia. This article briefly demonstrates the phramaco-potential of both these medications, their effects on patients based on a clinical observation and ongoing status of dosage to validate its implication.
    Keywords: Severe Acute Respiratory Syndrome2-CoV (SARS2-CoV); COVID-19; Hydroxychloroquine; Azithromycin; Choloroquine.

  • Comparison between whole exome sequencing and the FoundationOne   Order a copy of this article
    by Catherine Wellan, Hillary Rayo, Shashi Mehta 
    Abstract: This systematic review and meta-analysis examines the question of how effective the FoundationOne
    Keywords: tumor mutation burden; TMB; whole exome sequencing; WES; FoundationOne; targeted panels; next-generation sequencing; solid tumors; immune checkpoint blockade therapies; ICB; correlation; method comparison studies.

  • Identification of stroke using deepnet machine learning algorithm   Order a copy of this article
    by Abdulwahhab Alshammari, Noorah Atiyah, Hanoof Alaboodi, Riyad Alshammari 
    Abstract: Stroke is a disease that impacts individuals of all races, genders, and backgrounds. To combat the rising prevalence of the disease, the creation of accurate diagnostic tools is paramount. This paper uses two machine learning models, deepnet and decision tree, to assess the health record data from the Ministry of National Guard Health Affairs, Saudi Arabia. Deepnet outperformed the decision tree in accurately predicting stroke and stroke mimic. Deepnet achieved an accuracy of 92.35%, while decision tree achieved 90.8%. Future application of machine learning on stroke and stroke mimic diagnosis holds great potential in public health reform, patient empowerment, and minimising healthcare burden. This paper proposes building a national centralised semi-detection stroke data management framework to create a national platform in the diagnoses, acute, and long-term treatment of stroke.
    Keywords: stroke; machine learning; identification.
    DOI: 10.1504/IJMEI.2021.10041722
     
  • Hepatitis C virus and cirrhosis: an analysis of incidence and cost of management of disease in the USA   Order a copy of this article
    by Pratima Tandon, Dinesh Mital, Shankar Srinivasan, Riddhi Vyas 
    Abstract: This study utilised the national (nationwide) impatient sample (NIS), which is part of the healthcare cost and utilisation project (HCUP). A study was conducted to determine the incidence and cost of management of Hepatitis C (HC) and cirrhosis (C) patients. The incidence rate of HC is 34% and C is 30%. HC is higher in the age group of 21-65-year old's whereas C prevalent in people that are 66-80+ years. The HC count is higher for Medicaid and, C is for Medicare payment methods. HC patients have a high Emergency admission rate. C patients show higher rate to transfer to a short-term hospital, home healthcare, length of stay in the hospital, death rate during hospitalisation, frequency of biopsy procedures, charges for Liver Transplant, charges for admission to the trauma Centre as well as emergency admission. 62% of males have HC and 61% have C. For females 37% have HC and 39% C. HC and C is higher in Urban locations and low-income group. In average liver transplant is higher for the age group of 21 to 80 years. Asian people are less predisposed to getting HC.
    Keywords: hepatitis; hepatitis c; cirrhosis; SPSS; data analysis; HCUP; NIS; liver disease; cost; charges; length of stay; health outcomes; incidence of hepatitis.
    DOI: 10.1504/IJMEI.2021.10041773
     
  • Clinical decision support system for the diagnosis of Hepatitis C virus   Order a copy of this article
    by Pratima Tandon, Dinesh P. Mital, Shankar Srinivasan, Riddhi Vyas 
    Abstract: A new clinical decision support system (CDSS) was developed using Exsys Corvid for the diagnosis of Hepatitis C virus. This CDSS is medically accurate and can guide healthcare professionals through the diagnostic process. Corvid Exsys rule-based system is used for building automated expert systems. The software utilises backward and forward chaining techniques. All the questions asked by the system during the diagnosis process are based on the clinical literature. The system can guide a clinician through the diagnostic process to achieve hepatitis results. Decision-making expert system was successfully developed to diagnose the Hepatitis C virus.
    Keywords: hepatitis; Hepatitis C; clinical decision support system; CDSS; decision support systems; jaundice.
    DOI: 10.1504/IJMEI.2021.10041821
     
  • Identification and classification of schizophrenic speech using convolutional neural network for medical healthcare   Order a copy of this article
    by Akshita Abrol, Nisha Kapoor, Parveen Kumar Lehana 
    Abstract: Schizophrenia is a brain disorder that significantly affects the quality of life of affected individuals. One of its prominent symptoms is the induction of changes in the acoustics of the patients. In the absence of definite methods for its diagnosis, speech analysis can help in the preliminary screening of the patients. In this paper, an automated method using deep learning for differentiating between individuals with schizophrenia and psychosis from healthy individuals is suggested. Using convolutional neural networks with speech spectrograms as input, a classification accuracy of 87.01% has been obtained for levels of schizophrenia and 95.26% for differentiating between schizophrenic and healthy speech.
    Keywords: schizophrenia; convolutional neural network; CNN; deep learning; spectrograms.
    DOI: 10.1504/IJMEI.2021.10041905
     
  • Automatic speech classification in school children with cleft (and lip) palate   Order a copy of this article
    by Khaled Baazi, Mhania Guerti 
    Abstract: The automatic detection of emphatic occlusive [t] in pathological speech (PS) in schoolchildren with cleft lip and palate can provide diagnostic information to clinicians and speech therapists. We propose an automatic classification system for PS by the decision tree method (DT) to use it in rehabilitation in Algerian hospitals. Acoustic analysis was applied in order to extract the relevant acoustic characteristics of this type of PS. The results showed that the DT adapts well to the classification of the PS with a significant rate ratio (%) of the PS, for the case of the long vowels 87% and 85% for short vowels.
    Keywords: cleft palates; pathological speech; decision tree; classification; school children.
    DOI: 10.1504/IJMEI.2021.10041983
     
  • CIAE: class imbalance aware ensemble framework to predict drug side effects   Order a copy of this article
    by Kanica Sachdev, Manoj Kumar Gupta 
    Abstract: The binding of the drug compounds to certain biological off target proteins causes undesirable side effects or drug toxicology. The determination of drug toxicology at the early steps of drug development would help to economise on money as well as time. The paper proposes a novel framework, class imbalance aware ensemble (CIAE), for the identification of drug side effects using ensemble learning. It employs the related side effect information of the drugs to predict novel side effects. An eminent cause of the low performance of the machine learning based methods is the presence of class imbalance in the data. The proposed framework efficiently addressees this issue to improve the predictor performance. A comprehensive comparison of the method with the state of the art classifiers shows that the proposed framework yields better results for drug side effect determination.
    Keywords: biological targets; class imbalance; drugs; drug side effects; drug toxicology; ensemble classifier; machine learning.
    DOI: 10.1504/IJMEI.2021.10041984
     
  • An overview of early detection of Alzheimer's disease   Order a copy of this article
    by C.R. Nagarathna, Kusuma Mohanchandra 
    Abstract: Alzheimer's disease is a most common neurodegenerative brain disease characterized by a problem in thinking and loss in cognition memory judgment. One of the difficult tasks is to identify symptoms of Alzheimer's at its beginning stage. Once the disease is detected it is very serious to save the life of a patient. The current medical treatments cannot cure Alzheimer's but its early diagnosis can stop the progress of disease and make the patient lead their life. So it is necessary to diagnose Alzheimer's at an early stage using different modalities like EEG, MRI, and PET as they provide more promising results compared to a single modality In this paper, the progression symptoms, causes, modalities, and methods used for finding Alzheimer's disease is reviewed. The state-of-the- art literature is reviewed and the gaps identified in the literature are discussed.
    Keywords: Alzheimer's; early detection; cognitive; modalities.
    DOI: 10.1504/IJMEI.2021.10042418
     
  • Comparing the performance of machine learning techniques for low back pain diagnosis   Order a copy of this article
    by Hamid Bouraghi, Sorayya Rezayi, Soheila Saeedi, Rasoul Salimi, Meysam Jahani, Sajjad Abdolmaleki 
    Abstract: Low back pain is a global health problem that is a major cause of disability in developing and developed countries. Machine learning and data mining algorithms can be used to help diagnose this disease. This study aimed to determine the performance of different machine learning algorithms. Nine machine learning techniques, including support vector machine, decision tree, Naive Bayes, K-nearest neighbours, neural network, random forest, deep learning, auto-MLP, and rule induction, were used to modelling. This study revealed that the highest accuracy was related to the random forest (83.55%) and support vector machine (82.26%) classifiers. As a result, machine learning algorithms have good accuracy in low back pain diagnosis.
    Keywords: machine learning; data mining; low back pain; LBP; diagnose.
    DOI: 10.1504/IJMEI.2021.10043054
     
  • Efficient breast cancer detection using novel intensity partitioning-based clustering algorithm and multi-dimensional LSTM cyclic neural network   Order a copy of this article
    by Gul Shaira Banu Jahangeer, T.Dhiliphan Rajkumar 
    Abstract: Recently, early detection of breast cancer is significant to reduce the mortality rate, especially in women. Hence, the study aims to classify breast cancer from digital database for screening mammography (DDSM) dataset using partition and intensity based segmentation algorithm and modified convolutional neural network-long short-term memory (CNN-LSTM) classifier. Initially, pre-processing is performed using Gaussian filtering by taking the mammogram image. Then, it is segmented using a novel intensity partitioning-based clustering algorithm (IPCA). Further, feature extraction is performed and finally, classification is implemented using a novel multi-dimensional LSTM cyclic neural network (MLSTM-CNN). The analysis is performed to evaluate the efficiency of the proposed system and the outcomes explored its efficacy in breast cancer detection.
    Keywords: intensity partitioning; segmentation; long short-term memory; LSTM; cyclic neural network; CNN; breast cancer classification; digital database for screening mammography; DDSM; intensity partitioning-based clustering algorithm; IPCA.
    DOI: 10.1504/IJMEI.2021.10043507
     
  • Multi-task deep neural network models for learning COVID-19 disease representations from multimodal data   Order a copy of this article
    by Veena Mayya, K. Karthik, Krishnananda Prabhu Karadka, S. Sowmya Kamath 
    Abstract: Over the continued course of the COVID-19 pandemic, a significant volume of expert-written diagnosis reports has been accumulated that capture a multitude of symptoms and observations on diagnosed COVID-19 cases, along with expert-validated chest X-ray scans. The utility of rich, latent information embedded in such unstructured expert-written diagnosis reports and its importance as a source of valuable disease-specific information has been explored to a very limited extent. In this work, a convolutional attention-based dense (CAD) neural model for COVID-19 prediction is proposed. The model is trained on the rich disease-specific parameters extracted from chest X-ray images and expert-written diagnostic text reports to support an evidence-based diagnosis. Scalability is ensured by incorporating content based learning models for automatically generating diagnosis reports of identified COVID-19 cases, reducing radiologists’ cognitive burden. Experimental evaluation showed that multimodal patient data plays a vital role in diagnosing early-stage cases, thus helping hasten the diagnosis process.
    Keywords: COVID-19 diagnosis; clinical decision support systems; multi-task learning; healthcare analytics; deep convolution neural networks.
    DOI: 10.1504/IJMEI.2021.10043617
     
  • Computerised detection of autism spectrum disorder using EEG signals   Order a copy of this article
    by Aarti Sharma 
    Abstract: Autism spectrum disorder (ASD) is one of the most common neurological disorders. Detection of ASD is based on behavioural analysis made by clinician by conducting interviews with the parents of the child. This paper presents computer aided diagnostic tool to detect autism disorder. This paper presents the detection of ASD based on biological markers. An early diagnosis is essential to confirm that the child have ASD. In this paper, power spectral density and phase locked values have been extracted from gamma band of EEG signals for autistic and normal subjects. Gamma band from EEG signals is extracted using band pass filter. Significant decrement in these features is observed for the autistic subjects in comparison to normal subjects. Above findings are statistically validated though ANOVA. Anomalies in EEG signals can be used as potential biomarker for detection of ASD.
    Keywords: autism; electroencephalogram; EEG; gamma wave; computer aided diagnosis.
    DOI: 10.1504/IJMEI.2021.10043643
     
  • Biothermodynamics may be a highly useful tool to help medical practitioners to detect and cure morbidities   Order a copy of this article
    by Mustafa Özilgen 
    Abstract: Biothermodynamics may help clinicians to diagnose and cure many medical cases, slow down ageing, correct malnutrition and repair the distorted tissues. Thermodynamically detected decrease in cardiac or renal filtration efficiencies indicate a malfunctioning system. Thermodynamic assessment of the circulatory system after amputation and restoring the circulatory pressure drop may prevent deaths. Research on thermodynamics of fertilisation may help to medical practitioners to fight against infertility. Athletes of some sports have substantially shorter lifespan than the other people. Biothermodynamics may offers some help to those athletes via prescribing appropriate energy intake. Distortion of the energy management in the body is observed in various health problems, including cancer. The need for multidisciplinary research to achieve additional understanding of these phenomena has been suggested by numerous researchers. Biothermodynamics may offer the best tool to achieve this goal.
    Keywords: biothermodynamics; internal work performance; external work performance; energy and exergy efficiency; helping medical practitioners; diagnosing and curing morbidities.
    DOI: 10.1504/IJMEI.2021.10043802
     
  • Risk stratification of cardiovascular disease in type 2 diabetes using LDA and CNN for clinical decision management - a multi-centre study in eastern India   Order a copy of this article
    by Suparna Dutta, Saswati Mukherjee, Medha Nag, Sujoy Majumdar, Ghanshyam Goyal 
    Abstract: Approximately 72.9 million patients of type 2 diabetes mellitus (T2DM) in India are at a potential risk of cardiovascular diseases (CVDs), strokes and peripheral gangrene. CVD is a major cause of disability and death is one of the major areas of risk severity stratification study. Unlike well-known prediction score models of CVD herein, a unique assessment deep learning model is proposed to stratify the cardiovascular events in different risk grades in T2DM individuals This risk assessment tool can aid clinicians in decision management of CVD risk severity It is a retrospective cross-sectional observational study that stratifies risks using linear discriminant analysis (LDA) and convolution neural network (CNN). Class separability feature of LDA helps to achieve optimal performance. The model is externally validated in a cohort of 4,719 individuals with T2DM to assess performance heterogeneity across different settings.
    Keywords: convolution neural network; CNN; linear discriminant analysis; LDA; support vector machine; SVM; risk stratification; cardiovascular disease; CVD; type 2 diabetes mellitus; T2DM; India.
    DOI: 10.1504/IJMEI.2021.10043803
     
  • Global sensitivity analysis implications on the design of PF-04475270 model predictive control system   Order a copy of this article
    by Omer Hamid 
    Abstract: In this study, we adopted Pharmacokinetic-Pharmacodynamic (PKPD) of Pfizer ophthalmic drug 5-{3-[(2S)-2-{(3R)-3-hydroxy-4-[3-(trifluoromethyl) phenyl] butyl}-5-oxopyrrolidin-1-yl] propyl} thiophene-2-carboxylate (PF-04475270) from literature and performed a series of global sensitivity analysis (GSA) to identify the most influential PKPD parameters of PF-04475270. We used the sensitivity analysis for everybody (SAFE) toolbox GSA with two different well-established GSA methods. Our GSA analyses successfully identified the aqueous chamber transfer rates as high-ranking parameters in the comparison with all the other 11 different model parameters. Our data with prioritised parameters suggest the need for probing the aqueous drug concentration for future design of model predictive control (MPC) release system of PF-04475270.
    Keywords: global sensitivity analysis; GSA; model parameters uncertainties; predictive control; Iris-Ciliary body; pharmacokinetics; pharmacodynamics; sensitivity analysis for everybody; SAFE; model predictive control; MPC.
    DOI: 10.1504/IJMEI.2021.10043868
     
  • Cloud and online system of 3D printing for serving multi-client of hospitals and medical colleges in different locations   Order a copy of this article
    by Ignatius Luddy Indra Purnama, Alva Edy Tontowi, Herianto 
    Abstract: This manuscript presents a 3D printing method, especially human bone anatomy, in a cloud and online system. The human bone anatomy, specific skull and foot bone, of the patient’s digital imaging and communications in medicine (DICOM) file, is uploaded to the so-called 3DPNet-DICOM cloud. After printing, the 3DPNet-DICOM cloud sends a notification and the 3D printed bone model to the hospital by courier. The 3D images constructed from the DICOM file and the 3D printed model was identifiable with no significant dimensional errors. The result is to eliminate the human operators’ activity, and queuing at the order process.
    Keywords: 3D printing; bone model; cloud application; online application; DICOM; network access.
    DOI: 10.1504/IJMEI.2021.10043869
     
  • A conceptual model to improve the patient flow during COVID-19   Order a copy of this article
    by Abdesselam Bougdira, Asmae Mazti, Hayat Sedrati, Fayssal Jhilal, Saaid Amzazi, Chakib Nejjari, Hassan Ghazal 
    Abstract: In the COVID-19 context, hospitals across the globe endeavour to manage the unprecedented flow of patients. Challenges are related to the unexpected increase in patients, extra waiting time for cleaning and decontamination of inpatient beds, congested queueing in treatment areas, and waiting time in crowding rooms. This research introduces a new conceptual model to bring a broader, patient-centred, and traceability-based view of patient flow in a clinical setting in the context of COVID-19. The model design opts for four interrelated modes procedural, technical, operational, and contextual. These modes lay the basis of a patient flow solution before starting its development. The feasibility of the proposed model is shown through a usage scenario and developed user interfaces. Results have shown that a knowledge representation of the patient’s real-time information can enable better patient flow monitoring. In addition, it would support intelligent healthcare systems that reuse and share pieces of declarative knowledge. Such functionality would enhance the management of patient flow and improve dealing with the patient flow challenges imposed by the COVID-19 pandemic. Moreover, the presented proof of concept laid the ground for future applicability in various clinical settings facing similar infectious disease crises.
    Keywords: patient flow; clinical setting; COVID-19; traceability; ontology; artificial intelligence; conceptual modelling; hospital systems.
    DOI: 10.1504/IJMEI.2022.10044040
     
  • Analysis on effect of resampling techniques on cardiac arrhythmia classification using convolutional neural networks   Order a copy of this article
    by Rekha Rajagopal, V. Shyam Kumar 
    Abstract: Cardiac arrhythmia is a condition in which the heart beats at a faster rate or a slower rate instead of a regular rhythm. Medical professionals identify the category of cardiac arrhythmia by manually viewing the electrocardiogram (ECG) signal which is more time-consuming. There are possibilities of incorrectly identifying the arrhythmia categories due to practical difficulties in manually assessing the slight variations in the amplitude of ECG signals. This research work focuses on automatically categorising the heartbeats as normal beat, supraventricular beat, ventricular beat, fusion beat, and unknown beat using convolutional neural network. The class imbalance problem that arises because of a few numbers of heartbeats in certain arrhythmia categories is resolved using techniques such as synthetic minority oversampling technique (SMOTE), borderline SMOTE, SVM-SMOTE, and adaptive synthetic sampling (ADASYN). The proposed model demonstrates an average accuracy of 97.76 % in classifying arrhythmia classes using ADASYN technique. This model can help medical professionals in accurately diagnosing the arrhythmia classes.
    Keywords: cardiovascular disease; electrocardiogram; ECG; arrhythmia; convolutional neural network; CNN; class imbalance.
    DOI: 10.1504/IJMEI.2022.10044232
     
  • Machine learning approach to detect congenital heart diseases using palmar dermatoglyphics   Order a copy of this article
    by Y. Mahesha, C. Nagaraju 
    Abstract: The present article has proposed a machine learning method to detect congenital heart diseases (CHDs) such as atrial septal defect (ASD) and myocardial infarction (MI) based on the frequency of occurrence of palm patterns such as ulnar loop and whorl. The system has been developed based on SSD-MobileNet to detect ulnar loop and whorl patterns on palm image. The developed system has achieved an accuracy of 99.28% and 97.19% in the detection of ulnar loop and whorl respectively. Further, the work has been carried out to fix the threshold value on the number of ulnar loop and whorl patterns to detect CHDs such as ASD and MI. The receiver operating characteristic curve has been drawn and the area under curve is calculated for the detection of ASD and MI. These results have shown that the proposed method can be used as a screening model to detect ASD and MI.
    Keywords: ulnar loop; whorl; atrial septal defect; ASD; myocardial infarction; SSD-MobileNet.
    DOI: 10.1504/IJMEI.2022.10044266
     
  • Spotting congenital heart diseases using palm print based on faster R-CNN and spatial method   Order a copy of this article
    by Y. Mahesha, C. Nagaraju 
    Abstract: This paper proposes a machine learning method to detect congenital heart diseases (CHDs) using a palm pattern known as axial triradius. This article spreads light on three things. First, Faster R-CNN Inception v2 has been used to identify triradii on the palm image. Secondly, a novel spatial method has been proposed to select leftmost, rightmost and axial triradii. Finally, the angle at axial triradius has been calculated on the palm images of healthy people and of patients suffering from tetralogy of Fallot (TOF), atrial septal defect (ASD), ventricular septal defect (VSD) and coarctation of aorta (CoA). The result shows that the proposed method can be used as a screening method to predict CHDs.
    Keywords: axial triradius; CHDs; faster R-CNN; inception v2; spatial method.
    DOI: 10.1504/IJMEI.2022.10044267
     
  • Feasibility of automatic differential diagnosis of endodontic origin periapical lesions - a pilot study   Order a copy of this article
    by Jay Patel, Dinesh Mital, Vaishali Singhal, Shankar Srinivasan, Huanmei Wu, Sashi Mehta 
    Abstract: Periapical dental diseases (PDD) are one of the most prevalent dental diseases leading to tooth loss and poor quality of life. Differential diagnosis of periapical diseases is critical because the treatment plan depends on the diagnosis. The current diagnosis approach of PDD uses an eyeballing method mainly depending on clinicians’ expertise, any lack of which may lead to an inaccurate diagnosis. We developed an advanced image processing tool that can help clinicians for more accurately differential PDD diagnoses that leads to the correct treatment approach. Sixty periapical radiographs were processed by the tool and the differential diagnostic output was compared with manually annotated gold standard dataset. Our tool performed well with a 95% sensitivity, 89% specificity, and 93% accuracy in providing a differential diagnosis. Demonstrating such promising results, further studies should test the accuracy of this tool on a larger dataset get more definitive results for widespread deployment and use.
    Keywords: image processing; dentistry; periapical radiographs; automatic differential diagnosis; dental informatics; image processing; dental image processing; digital dentistry.
    DOI: 10.1504/IJMEI.2022.10044462
     
  • Real-time healthcare monitoring system through haversine distance calculation-based global positioning system   Order a copy of this article
    by Leo John Baptist, Linesh Raja, Suresh Shanmugasundaram 
    Abstract: In the present world, new technologies are highly-developed which drastically changes the conventional tasks of medical and the healthcare systems. Evolving mobile health (m-health) systems belong to these technologies with advanced data communication, artificial intelligence, deep learning, big data, cloud computing, and other machine learning approaches. Data collected using sensors are sent to the local databases via cellular networks which are the stored in the cloud. Data residing in the cloud or medical centres are used for analysis. Machine learning approaches are utilised for predicting the disease appropriately and for classification purpose. This research paper details the m-health systems, their framework with GPS assistance using machine learning. Moreover, this model can save the human life for some time as the ambulance location is tracked using GPS. Heart beat rate and temperature of patient are sensed and the information is sent to the hospital or to the mobile of the doctor.
    Keywords: global positioning system; GPS; healthcare; longitude; latitude; m-health.
    DOI: 10.1504/IJMEI.2022.10044463
     
  • Analysis of chest x-ray images using deep learning approaches   Order a copy of this article
    by Ruchika Arora, Indu Saini, Neetu Sood 
    Abstract: Common thorax diseases such as pneumonia, tuberculosis, are diagnosed with digital radiography, i.e., chest X-ray (CXR) images. This paper provides a glimpse of chest abnormalities classification and annotation methods for CXR images that improves work efficiency and diagnosis accuracy. At present, pre-trained models such as ResNet, DenseNet, and its variants have become important deep learning (DL) approaches for successful classification and detection of diseases. This detailed literature review highlights need for integration of both image and text metadata features for designing multi-label image classification systems for effective diagnosis of chest diseases. As novel coronavirus disease (COVID-19) causes lung problems so a new research frontier is to fight against COVID-19. This paper covers an insight into literature review of DL algorithms used for diagnosis of COVID-19, and also emphasises move from computer aided detection (CAD) to the clinic illustrating recent practices, problems, and up-to-date information on CXR image classification and annotation.
    Keywords: chest X-ray images; deep learning; machine learning; ResNet; DenseNet; image classification; disease detection; image captioning; COVID-19.
    DOI: 10.1504/IJMEI.2022.10044515
     
  • Empirical assessment of COVID-19 infections and information diffusion: a data science approach   Order a copy of this article
    by Isaac Kofi Nti, Adebayo Felix Adekoya, Owusu Nyarko-Boateng, Ponnadurai Ramasami 
    Abstract: The spread of the novel coronavirus disease, SARS-CoV-19 (COVID-19), has affected human activities everywhere, resulting in fear and panic among all age groups. Hence, this study implements a novel data science process to empirically model the daily reported cases and Google search queries in 14 countries. We observed a strong positive association (0.79-0.96) among reported cases of COVID-19 in the 14 countries. Furthermore, there is an inverse correlation of -0.18 to -0.62 between information diffusion on the virus and reported cases (new cases and deaths). Our outcome shows that contagious diseases are highly predictable using historical records from other countries and information spread on the disease.
    Keywords: data analytics; machine learning; data science; coronavirus; SARS-CoV-2; COVID-19; Goggle search engine; infections.
    DOI: 10.1504/IJMEI.2022.10044968
     
  • How health knowledge that can influence patient outcome   Order a copy of this article
    by Leandro Pereira, Carlos H. Jeronimo, André Salgado, Álvaro Dias, Renato Lopes Da Costa, Rui Gonçalves 
    Abstract: Since COVID-19 was declared a global pandemic, it has killed more than two million people worldwide. Some directly due to complications and symptoms of the virus others due to lack of resources to take care of all the patients affected by it. A proper triage can go a long way to an efficient resource allocation, which makes it extremely relevant to understand which factors can affect COVID-19 complications or mortality risk. With the analysis of survey data collect form front-line Portuguese doctors, it was possible to identify the main comorbidities and health issues that can influence patient outcome.
    Keywords: COVID-19; mortality risk; comorbidities; severity of symptoms; knowledge management.
    DOI: 10.1504/IJMEI.2022.10045013
     
  • Colorectal cancer risk factor assessment in Palestine using machine learning models   Order a copy of this article
    by Mohammad A. Z. Abu Zuhri, Mohammed Awad, Shahnaz Najjar, Nuha El Sharif, Issa Ghrouz 
    Abstract: Colorectal cancer (CRC) has a prevalence of 15% among men and 14.6% among women of all cancers. This research was carried out to assess behavioural risk factors that affected Palestinian reported CRC cases, and to make use of machine learning (ML) tools that might be used in CRC prediction, where the use of a public CRC classification and prediction tool based on accurate ML tools might help individuals in addressing their behavioural CRC risk factors and enhancing their engagement with their health. In this research, Palestinian dataset that consists of 57 predictors was collected, the dataset consists of 216 instances. Statistical models were used to determine the important features. The study found that the most important risk factors to consider are age, past medical history, diet behaviours, physical activity, and obesity. Consequently, ML models were applied to classify and predict CRC risk factors. Results showed that ANNs outperformed all models.
    Keywords: colorectal cancer; CRC; data mining; risk factors; machine learning; classification; Palestine.
    DOI: 10.1504/IJMEI.2022.10045260
     
  • Computational study of the progression of Alzheimer's disease and changes in hippocampal theta rhythm activities due to beta-amyloid altered calcium dependent ionic channels   Order a copy of this article
    by Akanksha Kaushik, Jyotsna Singh, Shilpa Mahajan 
    Abstract: Although, Amyloid beta (B-amyloid) and neurofibrillary tangles are the assay mark of Alzheimer’s disease (AD), cognitive decline is best concerned with synaptic loss, rather than tangles or plaques. The pyramidal neurons in Hippocampus are highly affected by AD. Therefore, pyramidal neurons are prime focus in our study. Pyramidal neurons have extensively developed calcium signalling, a phenomenon that controls the neuronal rhythms desirable for memory processing and cognition, for regulating wide range of functions like controlling rhythmic activities, information processing and memory binding. The focus of our work is to inspect the impact of B-amyloid on calcium signaling and progression of AD through computational study.
    Keywords: Alzheimer’s disease; calcium signalling; pyramidal neurons; hippocampus.
    DOI: 10.1504/IJMEI.2022.10045261
     
  • A deep neural network-based architecture for automated detection of COVID-19 from chest X-ray images   Order a copy of this article
    by Abul Abbas Barbhuiya, Ram Kumar Karsh, Rahul Jain, Hillol Phukan 
    Abstract: The COVID-19 pandemic has a severe adverse impact on people’s health, lives, and the worldwide global economy. It is only possible to identify positive COVID-19 instances if each country carries out sufficient tests. As suggested in prior studies, X-Ray images could be used as testing samples to develop a reliable and low-cost COVID-19 testing model. This paper introduces a deep learning-based end-to-end binary classification framework, Att-Net, for automated detection of COVID-19 cases using Chest X-ray images. In this work, we have adopted pre-trained ConvNet (VGG-16) with an attention module embedded with the VGG-16 architecture, which significantly improves the model’s performance. The proposed architecture is evaluated on the COVID-Xray-5k dataset. The suggested methodology obtains a state-of-the-art sensitivity of 98.5% and specificity of 99.4%. This work also presents a detailed performance analysis in terms of accuracy, sensitivity, specificity, precision, recall, and F-score. Furthermore, we have also generated the heat maps, which reveal the most anticipated regions infected by COVID-19 while learning for prediction by the CNN to validate the proposed architecture.
    Keywords: COVID-19; pandemic; deep learning; ConvNet; machine learning; transfer learning; feature extraction; X-ray images; healthcare management; CNN.
    DOI: 10.1504/IJMEI.2022.10045268
     
  • Identifying key gait parameters in gender recognition and classification performance analysis using machine learning algorithms   Order a copy of this article
    by Neha Sathe, Anil Hiwale, Archana Ranade 
    Abstract: Different gait parameters retrieved through pressure sensors, classification spatial, statistical, temporal and demographic (SSTD) model is suggested and tested for gender recognition and classification. Combination of spatial, temporal and demographic features along with performed descriptive statistics is use to train the model. Support vector machine, logistic regression and k-nearest neighbour classification results are tested and analysed for precision and recall. Step length and stride length with weight and height provides great performance in achieving accuracy. Classification results within range of 80% to 90% for selected dataset of healthy 80 subjects were achieved. Influence of stride length in female and step length in male recognition along with single support time is observed. Contribution of weight is also recognisable in classification accuracy. Behaviour of female recognition and classification provides clear results on selected features using SSTD model while precision and recall values whereas male recognition values are on lower end.
    Keywords: spatiotemporal parameters; support vector machine; SVM; k-nearest neighbour; KNN; logistic regression; LR; gender recognition.
    DOI: 10.1504/IJMEI.2022.10046133
     
  • An actigraph data-based early diagnosis of depression using ensemble classifiers   Order a copy of this article
    by C.D. Anisha, N. Arulanand, R. Rekha 
    Abstract: Depression is one of the severe mental disorders which prevails as one of the key symptoms in unipolar and bipolar disorder. An early diagnosis of depression can lead to quicker recovery. This paper proposes an artificial intelligence (AI)-based early diagnosis system for depression using the actigraph motor data. The key contribution of the paper is the 'ensemble classifiers' which is a type of machine learning (ML) model, a subpart of AI model, which improves the diagnosis of depression state by combining the predictions of various single classifiers. The result signifies that the proposed system with ensemble classifier AI model has an accuracy of 85% which is reliable and consistent than existing systems.
    Keywords: bipolar disorder; depression; actigraph; ensemble classifiers.
    DOI: 10.1504/IJMEI.2022.10045834
     
  • Tele-health consultations during COVID times - barriers and facilitators: a multi-state exploratory study from India   Order a copy of this article
    by Sree T. Sucharita, Chakrapani Chatla, Vikhram Ramasubramanian, R. Vaishika, S.M. Balaji, R. Pradeep, Aravind Manoharan 
    Abstract: Globally, COVID-19 has drastically derailed the clinical care for outpatient services. Healthcare professionals (HCPs) used various specially designed tele-health applications (THA) to cater to the needs of patients. We attempted to explore the barriers and facilitators in utilising THAs as part of a multi-state and multi-disciplinary exploratory sequential mixed methods study. Our study comprised of initial survey of HCPs using quantitative Google Forms Survey tool followed by personal (physical and phone-based) interviews of 30 selected/consented HCPs. Of the 98 HCPs responded to the survey, ~60% of had 1-2 telehealth consultations/day during the pandemic. Approximately 61% of the consultations were limited to < 60 minutes interactions. Less than half were obtaining consent and only one third were maintaining digital log record. Most of the younger HCPs especially women have continued their services through THAs and felt that THAs will continue in the future. Besides the limitations of generalisability, the results suggest enormous scope for THAs in future towards simplifying the processes for effective healthcare service delivery at lesser cost and time.
    Keywords: COVID-19; tele-health applications; THA; healthcare service delivery; digital platforms; India.
    DOI: 10.1504/IJMEI.2022.10045849
     
  • A new segmentation method for retinal pathologies detection in optical coherence tomography images   Order a copy of this article
    by Ben Khelfallah Amel, Messadi Mahammed, Lazouni Mohammed El Amine 
    Abstract: Diabetic macular oedema (DME) and age-related macular degeneration (AMD) are the leading causes of blindness in adults. The most significant signs of these diseases are appearance of exudates and change of retinal layer structure. Screening of these diseases is very important to prevent vision loss. In this work, a new method based on a genetic k-means algorithm for lesions detection is proposed. From the selected region of interest (ROI), four textural features are extracted and used to classify these two retinal diseases against the normal subjects using the SD-OCT images. From the experimental results found, the SVM gives better results for AMD and DME recognition. The mean accuracy, sensitivity and specificity values for the macular regions classification are 99.67%, 100% and 99.51% respectively.
    Keywords: OCT images; age-related macular degeneration; AMD; diabetic macular oedema; DME; features extraction; classification; genetic algorithms.
    DOI: 10.1504/IJMEI.2022.10046139
     
  • Automated classification of cervical cells using integrated VGG-16 CNN model   Order a copy of this article
    by Rajesh Yakkundimath, Varsha S. Jadhav, Basavaraj S. Anami 
    Abstract: The most popular method for early cervical cancer screening and detection is the Pap-smear. Automatic analysis of Pap-smear images using computer technology will help in the accurate classification of cervical cancer cells. In this paper, a deep learning approach based on VGG-16 convolutional neural network (CNN) model integrated with support vector machine (SVM) classifier is proposed to identify and classify the cervical cells. A deep convolutional generative adversial network (DCGAN) framework is employed to generate the required synthetic Pap-smear images. The best average classification result of 96.24% is achieved on the held-out dataset comprising 16,124 images belonging to five classes of cervical cells.
    Keywords: cervical cancer; Pap-smear images; data augmentation; classification.
    DOI: 10.1504/IJMEI.2022.10046152
     
  • Feature importance analysis for a highly unbalanced multiple myeloma data classification   Order a copy of this article
    by Rima Guilal, Nesma Settouti, Gonzalo Martínez-Munoz, Mohammed Amine Chikh 
    Abstract: Multiple myeloma (MM) is a hematological cancer associated with abnormal plasma cell proliferation. Its diagnostic process is long because it is very difficult to discover it at an early stage. This paper presents an approach to aid in MM diagnosis and staging. Tree-based ensemble learning methods are used to measure the features importance in models constructed for predicting MM stages. Comparative analysis showed that random forest outperformed other algorithms with an accuracy of over 97%; however, XGBoost gives a ranking of features considered most prognostic for MM staging. A discussion of results with specialists in hematology supported and validated our proposed study.
    Keywords: blood cancers; multiple myeloma; prognostic factors; ensemble learning; feature importance; unbalanced data; grid search.
    DOI: 10.1504/IJMEI.2022.10046878
     
  • Effectiveness of machine learning for mental health: observing the mental state of Bangladeshi people   Order a copy of this article
    by Sayda Umma Hamida, Narayan Ranjan Chakraborty 
    Abstract: Analysing and finding the most used AI applications in the mental health sector and advising appropriate directions for advanced research is the intention of this research. With this purpose, authors commenced a systematic review by analysing selected 31 articles and found several neuroimaging and recognising technologies in real life for checking brain abnormalities. Besides, it revealed from the study that bot is the most used AI assistant in digital care. However, the authors surveyed the young people (aged between 19-29) of Bangladesh to identify mental disorders like as- anxiety, depression, and PTSD. The authors used Python to analyse the dataset, find correlations, and applied machine learning classification algorithms (e.g., decision tree, support vector machine, random forest) to measure the accuracy. The researchers explained a few threats of mental instability in their findings and offered several directions for future research using virtual and real-life AI technologies.
    Keywords: AI; mental-health; anxiety; depression; PTSD; Chatbot.
    DOI: 10.1504/IJMEI.2022.10046879
     
  • New bio-inspired approach for deep learning techniques applied to neonatal seizures   Order a copy of this article
    by Mohamed Akram Khelili, Sihem Slatnia, Okba Kazar, Seyedali Mirjalili, Samir Bourekkache, Guadalupe Ortiz, Yizhang Jiang 
    Abstract: Neonatal seizures are a common emergency in the neonatal intensive care unit and their detection using electroencephalography (EEG) recording is one of the biggest challenges that neurologists face. Even though using artificial intelligence methods such as deep learning for computer vision can help to solve these problems, time consumption, complexity, and overfitting or underfitting of the model still limit the application of deep learning. In order to produce a real-time system that can detect neonatal seizures using EEG and solve the problem of the lack of availability of neurologists, a convolution neural network-based marine predator algorithm system is proposed.
    Keywords: neonatal seizures; electroencephalography; EEG; artificial intelligence; deep learning; convolution neural network; CNN; parallel metaheuristic optimisation; marine predators algorithm; MPA; genetic algorithm.
    DOI: 10.1504/IJMEI.2022.10046880
     
  • Neoteric machine learning approaches to diagnose the state of carotid artery   Order a copy of this article
    by Hariharan Anantharaman, Navendu Chaudhary, Vimal Raj 
    Abstract: Develop machine learning models based on the information, data elements and images captured on a carotid ultrasound. Initiated with capture, collation and compilation of comprehensive carotid ultrasound reports of patients. Next analyse, extract, clean and compile data for the development of model. In this neoteric approach, a set of supervised algorithms and image-deep learning algorithms are implemented, different models built and tested. Performance of all the models is par excellence with a majority delivering accuracy over 75%. All the models, based on the varied machine learning algorithms, delivered acceptable and consistent accuracy - few models have even surpassed and delivered superior accuracy.
    Keywords: classifier; supervised learning; carotid ultrasound; stenosis; machine learning; deep learning.
    DOI: 10.1504/IJMEI.2022.10046881
     
  • An energy efficient reconfigurable architecture for multi-lead ECG signal compression   Order a copy of this article
    by Vinod Arunachalam, N. Kumareshan 
    Abstract: The most used non-invasive diagnostic technique for a wide range of cardiac disorders is an ECG, which records the heart’s electrical activity over time. Compressed ECG signals are a necessary part of most electronic health systems to store and transmit data across long distances. The field programmable gate array (FPGA), a high-speed parallel compute unit, and customisable software capabilities are available with reconfigurable architecture. Consequently, this architecture is suitable for devices like ECGs, which require precise real-time computing for multi-channel signal processing. The Xilinx Zynq 7.000 SoC development board used in this work has an FPGA-based reconfigurable signal processing unit. When compressing data, the method uses fast fourier transformation (FFT). It is possible to achieve a 90% compression rate with this system running in real-time and with minimal to no signal distortion. This method is also the only one in the industry that can reduce high-frequencynoise.
    Keywords: ECG; reconfigurable architecture; health system.
    DOI: 10.1504/IJMEI.2022.10047060
     
  • Biotechnical neural network system for predicting cardiovascular health state using processing of bio-signals   Order a copy of this article
    by Sergei Filist, Riad Taha Al-Kasasbeh, Olga Vladimirovna Shatalova, Mohammad Hjouj Btoush, Manafaddin Namazov, Ashraf Adel Shaqadan, Mahdi Alshamasin, Nikolay Korenevskiy, Saleh Aloqeili, Maxim Borisovich Myasnyankin 
    Abstract: In this study, for the early diagnosis of cardiovascular diseases, a multimodal classifier is built, in which three groups of heterogeneous data are used. The data is classified by autonomous intelligent agents with subsequent aggregation of their solutions at the next hierarchical level of classification. As one of the lower-level classifiers, a classifier is used, built on descriptors obtained on the basis of monitoring and analysing the evolution of the amplitudes of the harmonics of the 0.1 Hz systemic rhythm. The presented architecture of the multimodal classifier showed an increase in the accuracy of the diagnostic efficiency by 11%.
    Keywords: system rhythms; signal demodulation; electrocardiosignal; spectral analysis; neural networks.
    DOI: 10.1504/IJMEI.2022.10047451
     
  • A review on applications of near infrared spectroscopy technique for neonatal monitors   Order a copy of this article
    by Pooja Gohel, Vijay Dave 
    Abstract: In current scenario, to avoid infant mortality especially preterm infants with unknown cause is a big challenge for neonatal hospital personnel. In spite of observation with currently available monitoring devices, there has been remarkable infant death happening at the hospital site. So, need for a device arises that monitor major vital parameters reflecting assessment of important organs such as brain and heart on a continuous basis. Near infrared spectroscopy (NIRS) is the recent technique that measures vitals such as regional cerebral oxygenation (rScO2), tissue perfusion, and related demand-supply of blood at specific organ in continuous, non-invasive, real-time quantitative manner. Many articles have been published showing wide applications of NIRS technique to compare the recorded rScO2 signal under various diseases conditions. This paper gives review of NIRS based measurement of vitals for infants, its applications and effectiveness.
    Keywords: near infrared spectroscopy; NIRS; preterm infant; tissue perfusion; neonatal monitors; regional cerebral oxygenation.
    DOI: 10.1504/IJMEI.2022.10047493
     

Special Issue on: Security and Privacy Concerns on Electronic Health Records

  • Integration of Intelligence in software development process for implementing a secure healthcare system - a review   Order a copy of this article
    by N. Asha, Siva Rama Krishnan, J. Gitanjali 
    Abstract: The implication of artificial intelligence (AI) has made substantial evolution in the modern environment. It has innovatively transformed the technical world and has assimilated everything in the real life. The impact of AI in the software development and process has significantly improved the progression with its cognitive behaviour. In modern-days, there is a remarkable increase in the data management; computational vitality has risen at reduced cost. Lot more breakthroughs are happening in technology, AI is performing all these seamlessly. To present the overall role of AI in software development and process, we have analysed the state-of-the-art of AI in software development for privacy in healthcare. The work is exemplified by recent advances in product development process and AI for securing medical data. Further, we also investigate on various phases of software development process where AI can be embedded for developing an efficient and secured healthcare system especially in the analysis of electronic medical records (EMR). We also discuss the challenges in incorporating AI in healthcare application.
    Keywords: artificial intelligence; AI; security; privacy; healthcare; fault detection; electronic medical records; EMR.
    DOI: 10.1504/IJMEI.2021.10041120
     
  • Developing a biotechnical scheme using fuzzy logic model for classification of severity of pyelonephritis   Order a copy of this article
    by Nikolay Korenevskiy, Seregin Stanislav Petrovich, Riad Taha Al-Kasasbeh, Ayman Ahmad Alqaralleh, Sofia Nikolaevna Rodionova, Ashraf Adel Shaqadan, Ilyash Maxim Yurievich, Mahdi Salman Alshamasin 
    Abstract: The aim of the work is to develop fuzzy logic model to process health data involving oxidative indicators in patients with pyelonephritis to predict the severity level of pyelonephritis as severe and purulent forms. A 13 immunity and oxidative health indicators (lipid peroxidation) are used for classification of disease severity. A control sample of patient's is analysed to develop class's and experienced physicians are consulted to modify considered class limits. The fuzzy logic model gives high accuracy in diagnosis of serous and purulent pyelonephritis in patients with urolithiasis. Verification of the results of the operation of the obtained decision rules on the control sample showed that the proposed method's diagnostic efficiency reaches 93%, which is acceptable for use in medical practice.
    Keywords: pyelonephritis; serious and purulent form; fuzzy logic; mathematical models; differential diagnosis; oxidative status.
    DOI: 10.1504/IJMEI.2021.10041822
     
  • Detecting obstructive sleep apnea by extracting multimodal HRV features using ensemble subspace discriminant classifier   Order a copy of this article
    by Nivedita Singh, R.H. Talwekar 
    Abstract: Obstructive sleep apnea disorder is very peculiar sleep disorder which is triggered because of rapid and repeated transition of breathing. Hypopnea is also known as partial blockage of respiration during sleep. Polysomnography is gold standard to detect OSA but it is very expensive and complex which motivates us to detect OSA through multimodal heart rate variability (HRV) feature analysis using single channel ECG. The comparison among three classifiers SVM, weighted KNN and ensemble subspace discriminant are investigated for OSA detection. The accuracy obtained by the ESD classifier is 100%. True positive rate (TPR) and the true negative rate (TNR) have been attained 100% which is best suitable classifier for our experiment.
    Keywords: obstructive sleep apnea; ensemble subspace discriminant; heart rate variability; multimodal features.
    DOI: 10.1504/IJMEI.2022.10044042
     
  • Cloud-based electronic health record sharing and access controlling blockchain architecture using data de-identification method   Order a copy of this article
    by Munshi Rejwan Ala Muid, Afrin Jubaida, Md. Mehedi Hasan Onik, Hamim Hamid 
    Abstract: Electronic health record (EHRs) demands the highest privacy and security as the accuracy of medical research and service largely depend on the integrity of it. Intruders can tamper sensitive protected health information (PHI) or personally identifiable information (PII) purposefully. This study aims to use blockchain technology to build a complete EHR sharing and tracking system, such that even after access is granted to researchers or medical personnel, EHRs remain unaltered and the identities of patients are hidden. This work uses a traditional cloud storage system to store information while saving the indexes of EHRs in a chain to ensure PHI integrity. Additionally, the data sharing is secured by applying a randomly selected data de-identification method that guarantees integrity and trackability of user data anonymously. Finally, this work demonstrates a complete architectural implementation (using Hyperledger and a private blockchain network) of the proposed EHR sharing and access controlling by using two levels of chain (two distinct ledgers) that ensures an efficient sharing platform of PHI.
    Keywords: electronic health records; EHR; blockchain; private cloud; data de-identification; security; access control.
    DOI: 10.1504/IJMEI.2022.10046882