International Journal of Medical Engineering and Informatics (51 papers in press)
A Distributed Integration System Enabling Electronic Health Records: An Italian Experience
by Cinzia Muriana, Giulio Gilia, Vincenzo Mistretta, Tommaso Piazza, Giovan Battista Vizzini
Abstract: Today distributed systems are commonly employed in patient data
management to improve the care process. Moreover, distributed systems can be
usefully applied to realise lean and flexible architectures that facilitate health
care structure interoperability. Systems used in health care structures are
characterised by proprietary data flow formats and encoding systems, which
hinder the possibility of sharing data in a standard format, and extracting
atomic data for further analysis. This paper describes the architecture of a distributed interoperability system able to structure flow data into standard
formats and send them to an advanced electronic health record designed as part
of the Smart Health 2.0 project (PON04a2_C). The use of the distributed
interoperability system within the experimental phase of the electronic health
record highlights the flexibility of the proposed solution in meeting the reality
of the health care structures involved.
Keywords: distributed systems; informatics health care solutions; CDA2;
electronic health records; medical informatics; medical engineering.
A Novel Patient Friendly IT Enabled Framework for Selection of Desired Health Care Provider
by Salaja Silas, Elijah Blessing Rajsingh
Abstract: The healthcare providers have tremendously grown during the past decade. Selecting a healthcare provider based on patients preference is really a great challenge. The purpose of this paper is to provide an IT enabled framework that will help the patients to find a suitable healthcare provider based on their preferences. The patients preferences in selecting a healthcare provider differ based on their geographical region, gender, age and socio-economic background. Discovering a set of feasible healthcare providers and selecting the most appropriate healthcare provider based on the patient preferences can be modeled as a multi-criteria decision making problem. In this article, a novel patient friendly framework is proposed for selecting the best healthcare provider. Even with a huge data set, the results show that the proposed framework is robust, reliable, faster and effective. The analysis reveals that the number of preferences and the healthcare providers influence the healthcare selection time. Case studies on 500 patients from different geographical regions reveal that the proposed framework is effective.
Keywords: Healthcare provider; multi-criteria decision making; service selection; ELECTRE.
Real-Time Signal Processing of Photoplethysmographic (PPG) Signals to Estimate the On-Demand and Continuous Heart Rate by spectral Analysis
by Madhan Mohan, Nagarajan V, Vignesh J.C
Abstract: In Healthcare applications, heart rate is one of the vital signs which give the health informatics of a person and also it is an important parameter to monitor for athletes and fitness enthusiasts during their workouts. In earlier days, the Heart Rate monitoring devices like ECG are very expensive and required more hardware resources. But nowadays, the evolution of PPG sensors help to develop low cost Heart Rate monitoring devices with minimal hardware resources. PPG sensor based Heart rate monitoring devices are non-invasive low cost devices and it can be used in real time applications to estimate the heart rate of a subject. In this paper, an efficient algorithm is proposed to find the Heart rate using frequency spectrum analysis on PPG signals. The proposed algorithm uses minimum hardware so it can be targeted in real time embedded platforms. Using the proposed algorithm, the Heart rate is calculated with a pass percentage of 99.2, Mean Absolute Percentage Error (MAPE) of 1.59%, Mean Absolute Error (MAE) of 1.20 BPM and Reference Closeness Factor(RCF) of 0.989. The First reliable Heart rate output from the algorithm comes in 6.5th second, which is the minimum possible time. The algorithm operates with a speed of 2 MIPS and with a memory of 18 KB. So the proposed method can be integrated to any low cost real-time embedded platforms to accurately measure the Heart Rate.
Keywords: Heart rate; Photoplethysmography (PPG); Frequency domain; Smoothing; Decimation; FFT.
Detection of Abnormal Blood Cells by Segmentation and Classification
by Abdellatif Bouzid-Daho, Mohamed Boughazi, Eric Petit
Abstract: Leukemia is a cancer of the hematopoietic cells. The detection of abnormal cells before cancer degeneration is a medical problem. The aim of our work is to obtain maximum recognition rate of leukemia. We propose the development of a system based on mathematical morphology and k-means methods capable of segmentation, classification, and detection of the cancerous blood cells. This allows the characterization and the description the cancerous region, which is an important task in the interpretation and diagnosis of pathologies present in blood. The segmentation was carried out using an efficient and fast algorithmic processing. It turns out that the proposed system shows to better segmentation and classification for tested images. The obtained experimental results are very encouraging which help hematologists for identification of abnormal blood cells.
Keywords: Leukemia; k-means; segmentation; classification; diagnostic; abnormal blood cell.
Study of Speech Enabled Healthcare Technology
by Saswati Debnath, Pinki Roy
Abstract: Cost and quality of healthcare are the most challenging requirements in todays fastest growing medical technology and to meet these requirements Automatic Speech Recognition (ASR) is one of the blessings to the medical world. Automatic speech recognition offers the potential to dramatically improve the cost and quality of healthcare service; many developments took place in national and international standard like e-prescription, clinical documentation, speech recognition in radiology, pathological speech signal analysis etc. Speech based healthcare application also helps to access remote healthcare service. Recently large number of hospitals and doctors use these types of medical technology to deliver better services to the patient. This paper presents a review of recent advancement on medical technology based on speech recognition. It covers some of the speech based healthcare research and software in national and international standard, the implementation framework and application and also this paper come up with a new idea that is identify the symptoms of diseases using machine learning. The symptoms are given through speech. The experimental result shows the accuracy of identifying the symptoms through speech recognition. The aim of the research is to improve medical technology using machine learning and speech recognition technology. In future it will help people for remote health care where doctor will not physically available and reduce workload pressure of doctor.
Keywords: Medical technology; Automatic speech recognition (ASR); Speech features; Machine learning.
Automatic Ostia Detection in CTA Volume Data: A Comparative Study
by Noha Seada, Mostafa G.M. Mostafa
Abstract: Objectives: Automatic coronaries ostia detection is implemented using two different methodologies: Template Matching and Corner Detection. Methods/Analysis: The proposed methods used for the ostia detection are selected for implementation after studying ostia anatomical features and observing their appearance on a segmented ascending aorta contour. Findings: Template matching is implemented for coronaries ostia detection, since they have a special pattern on a segmented ascending aorta contour. Therefore two templates are identified, one for the right and one for the left coronaries ostia. These two templates are matched across the input volume to detect the coronaries ostia. For corner detection; Harris corner is implemented to detect coronaries ostia as corners appearing on the ascending aorta. It is applied on the volume to detect corners and ostia points are correctly located based on ostia anatomical features that automatically identifies the ostia points. Novelty/Improvement: The two methods used for automatic ostia detection succeeded to detect the coronaries ostia points in all test cases and results are validated versus a ground truth. Although both approaches succeeded in ostia detection; the template matching accuracy and processing time are better than that of the Harris corner detection. Moreover, the two methodologies presented for comparison; give competitive accuracy and processing time with respect to other previous work for ostia detection and this proves the correctness and efficiency of the methodologies.
Keywords: Template Matching; Automatic Ostia Detection; Computed Tomography Angiography (CTA).
Learn Quest A Virtual Reality based system for training Autistic Kids
by Shriram K Vasudevan, Abhishek S N, Swathi S, Akshaya Lakshmi, Anandram S
Abstract: Childhood is the best part of life. A child enjoys anything and everything in this world without any discriminations. It is also the period of rapid brain development, where the kids learn the stepping stones for life. Autism Spectrum Disorder (ASD) is a group of developmental disabilities that causes major impairments in social, communication and behavioral aspects of the children. Unlike other children, an autistic kid finds it difficult to go along and communicate with others. Social interactions and communication are the major aspects for peaceful living of any human being, and these children face difficulties in the same. So, we have developed a virtual reality based game engine that would attract the children and make them enjoy interacting with it while increasing the chance for learning. As the children continues playing this game, their learning and interacting ability increases nullifying some effects of autism, thus making it a boon for these kids.
Keywords: Virtual Reality; Autism; 3D; game; interaction; Gesture; Leap Motion Sensor.
Mechanics of the septal wall may be affected by the presence fibrotic infarct in the free wall at end-systole
by Fulufhelo Nemavhola
Abstract: The relationship between infarct mechanics and ventricular function of septal wall in the presence of fibrotic infarct in the free wall remains poorly understood. This study compares the mechanics of the septal wall in the healthy and fibrotic infarcted rat hearts models during active ejection. Finite element models of rat heart were developed to study the ventricular mechanics of the septal wall in the presence of fibrotic infarct in the left ventricular free wall. To simulate active contraction, three dimensional constitutive Fung model was implemented in Abaqus
Keywords: septal wall; myocardial mechanics; heart computational models; fibrotic infarct; infarct modelling.
Optimized DBN for Effective Enhancement of Ultrasound Images with pelvic Lesions
by Sandanand L. Shelgaonkar, Anil B. Nandgaonkar
Abstract: Nowadays, the ultrasound modality is the current research areas for lesion analysis. Moreover, it is renowned; the ultrasound images exhibit hassle- free utilization as well as cost effectiveness. In the literature, merely a small number of reliable contributions have been reported from the females for analyzing the pelvic lesions. However, the literature lags with adopting a prominent enhancement procedure to facilitate the diagnosis process. This paper adopts an Optimized Deep Belief Neural (ODBN) network for enhancing the US image of pelvic portions. It considers the higher order as well as lower order statistical characteristics of the image to define the appropriate filter band for image enhancement. The lower order features are optimized to use with the higher order features so that the optimal usage of features is reported here. To optimize the lower order features, an advanced optimization search algorithm named Grey Wolf Optimizer algorithm (GWO) is exploited. The ODBN learns the optimized features and the noise characteristics for precise prediction of the filter bands, which enhance the image substantially over the conventional filter bands. The performance of the proposed method is compared with the conventional methods using the benchmark as well as real-time US images of pelvic lesions. The quality of enhancement is ensured using the renowned measures such as PSNR and ESSIM that exhibit the performance of the proposed approach.
Keywords: ultrasound; PSNR; ESSIM; GWO; DBN.
An innovative technical solution to avoid Insomnia and Noise-Induced Hearing Loss
by Shriram K V, Ikram Shah, Sriharsha Patallapalli, Karthikeyan S, S. Subhash Chandran, U. Adithya Bharadwaj
Abstract: The major challenge these days with the increased usage of the mobile phone is loss of sleep (insomnia), increased stress and finally more damages to the health and mental wellness. Most of us have the habit of even keeping the phone underneath the pillow while sleeping. There are people who are now treated for mobile addiction. People are there to use a phone to get sleep sooner. Sleep time is meant for the brain to rejuvenate, organize thoughts and relax. If the sleep patterns are disturbed due to a continuous external commotion, it will result in the subconscious to spend energy and brain space. Hence, it is wise to switch off the music after the person sleeps which most of us do not do as we are already slept by then. Also, wearing headphones for an hour will increase the bacteria in your ears by 700 times. Many researches in the past prove that music is a solution to reduce stress and to bring sleep. But, we, the users are using headphones for music and it causes some dangerous side effects including affecting the sleep and noise-induced hearing loss. There is a strong need to avoid unnecessary health problems which are meant to be avoided. Here, we propose a system which will ensure that the music player is stopped after understanding that the person using it has slept and he no more needs the tunes thereby not disturbing the deep sleep and preventing insomnia and noise-induced hearing loss. Novelty is what we present in this research through making sure if the person has really slept or not. Our innovative system switches off the audio automatically when a person falls asleep. The same can be expanded and improvised to provide analytics to the user as in how many hours did he sleep peacefully, how is his body tuned towards listening to music, how long does he need music, under what conditions does he over listen or under listen etc.
Keywords: Sleep monitoring; Headphones/ Earphones ; Wearables; Heart rate ; Pulse; Mobile Application; Multimedia Playing; Insomnia; NIHL.
Health Panel: a Platform Useful to Physicians for Fast and Easy Managing of FPGA-based Medical Devices
by Agostino Giorgio
Abstract: The Design of Medical Devices is frequently based\r\non Field Programmable Gate Arrays (FPGAs). This is especially\r\ndue to their unique properties in order to fast prototyping and\r\ntheir great capabilities in Digital Signal Processing (DSP). The\r\nproblem arises to make physicians able to manage such devices\r\nespecially to set and program during prototyping, experimental\r\nand validation processes of new medical devices. Therefore, the\r\nobjective of this paper is the description of a platform, named\r\nHealth Panel, in its hardware and software components,\r\novercoming these issues. The design methods and the test and\r\ndebug methods are also detailed. The hardware is an Embedded\r\nSystem (ES) and the software, developed in Matlab environment,\r\nacts as user friendly interface making the physician able to\r\nmanage quick and easy the FPGA. Results of tests and of an\r\naccurate debug of the platform are described. The platform aims\r\nat providing a significance contribution for designing hardware\r\nand software interfaces for an easy and quick use of FPGA-based\r\nmedical devices. Conclusions and final remarks provide\r\nsuggestions for future developments of the platform.
Keywords: FPGA; Embedded Systems; Digital Signal
Processing; Matlab; Medical Devices; Electrocardiogram,
Spectro-Temporal Analysis of Electromyogram Signals (EMGs)
by Meziani Fadia, Rerbal Souhila, Debbal Sidi Mohammed El Amine
Abstract: Electromyography (EMG) signals can be used for clinical/biomedical applications. Is a very important biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular. Individual muscle fiber action potentials are sometimes acquired using wire or needle electrodes placed directly in the muscle. The combination of the muscle fiber action potentials from all the muscle fibers of a single motor unit is the motor unit action potential (MUAP) which can be detected by a skin surface electrode (non-invasive) located near this field, or by a needle electrode (invasive) inserted in the muscle. The simple detection with electrodes has been recognized for a long time as an important tool for the diagnosis of neuromuscular diseases, although its accuracy is still insufficient to diagnose some neuromuscular diseases (Myopathy and Neuropathy). It does not enable the analyst to obtain both qualitative and quantitative characteristics of the EMG signals. The efficiency of diagnosis can be improved considerably by using modern digital signal processing techniques. Therefore, these last can provide useful and valuable information on these signals. The aim of this study is to analyse EMGs signals using temporal and frequency analysis. This analysis is based on temporal parameters such as :amplitude, energy, entropy... and frequency parameter such as median frequency and mean frequency by using the FFT and the non-linear information by using the bispectrum analysis , can provide a wide range of informations related to the type of signal (normal and pathological).
Keywords: Electomyogram; EMG; signal; time-frequency; analysis; parameters; pathology.
Generating a stable primary schedule for an integrated surgical suite
by Asie Soudi, Mehdi Heydari
Abstract: Efficient utilization of operating room (OR) is a common anxiety of surgical suit manager which necessitate an effective planning and scheduling of surgeries. In this paper we investigate predictive / reactive scheduling of an integrated surgical suite in the form of a two-stage hybrid flow shop scheduling problem (HFSP). The deterministic model comprises both assignment and sequencing decisions for elective surgeries. By further considering shared capacity between elective and emergency patients, a chance constrained programming model is extended for the first time to cope with uncertain disruption. It is shown that how a chance constrained model will reduce to just considering an augmented surgery which processing time depends on distribution function of emergency surgery processing time and confidence level of scheduler. Two new important measures in reactive scheduling literature, 'stability' and 'robustness' are taking into account in surgical suite scheduling for the first time. Computational results demonstrate the efficiency of primary schedule generated by extended chance constrained programming model as well as the effectiveness of new measures in hospitals. As the chance constrained model is NP-hard, a decomposition heuristic algorithm based on tabu search (TS) is proposed to cope with problems of real size.
Keywords: Stability and robustness; predictive / reactive scheduling; hybrid flow shop; integrated surgical suite.
Adaptive Improved Binary PSO Based Learnable Bayesian Classifier for Dimensionality Reduced Microarray Data
by Barnali Sahu, Satchidananda Dehuri, Alok Jagadev
Abstract: This article presents, an adaptive improved binary particle swarm optimization based learnable Bayesian classifier for dimensionally reduced microarray data. In the first fold of this two-folded work, the problem of dimension is reduced by unsupervised method of feature reduction. We apply k-means clustering algorithm on the microarray data to group functionally redundant genes followed by application of signal-to-noise-ratio ranking technique to generate an intermediate feature subset consisting of most relevant and non-redundant feature subsets. In the second fold, the feature subset has been given to adaptive binary particle swarm optimization based learnable Bayesian classifier for simultaneous selection of features and classification. We conduct an extensive experimental work on a few benchmark datasets to validate its classification accuracy with and without reducing the dimensionality of micro-array data. It was observed that our method is not only accepted as a good classifier over methods, which are considered here for comparison but also be treated as an alternative method of reducing dimension of the problem.
Keywords: Microarray data; Feature selection; Signal-to-Noise-Ratio; Classification; Bayesian classifier; Adaptive PSO.
Product Unit Neural Network trained by an Evolutionary Algorithm for diabetes disease diagnosis
by Radhwane BENALI, Nabil Dib, Fethi Bereksi Reguig
Abstract: Diabetes disease occurs when the level of glucose in the blood becomes higher than normal because the body is unable to produce the insulin which is needed to regulate glucose. In this study, a new classification method for the diagnosis of diabetes disease was developed. This method is based on a special class of neural network known as product-unit neural networks (PUNN) which was trained by an evolutionary algorithm (EA). We have used EA in order to determine the basic topology of the structure of the PUNN, and to estimate its coefficients weights. The performances of the proposed classifier were evaluated through the sensitivity, the specificity and the classification accuracy using both conventional and 10-fold cross-validation method using the Pima Indian Diabetes (PID) dataset. Obtained results reveal that the proposed approach outperforms several famous and recent methods existing in the literature for diabetes disease diagnosis.
Keywords: Product Unit Neural Network (PUNN); Evolutionary Algorithms (EA); Diabetes disease diagnosis; Pima Indian diabetes (PID).
Development of a Serious Game to Enhance Assistive Rehabilitation
by Mauro Bellone, Andrea Martini, Francesco Argese, Piero Cirillo, Italo Spada, Antonio Cerasa
Abstract: The aim of this study is to investigate novel assistive technologies
based on serious gaming for the assessment of postural control and motor
rehabilitation. Previous research already demonstrated that rehabilitation and
physical activities can improve the quality of life of patients, and virtual reality
applications may act as good additional companions during the therapeutic
sessions. Indeed, workout routines supported by serious gaming encourage
patients to train harder, bringing the therapy towards a pleasant game into a
virtual environment. During the game, sensors track the movements of the
players and transfer the data to software components that record a database
from which patients progress can be determined. The acquired measures are
interpreted in the form of new biomarkers, which enable the assessment of
postural control. Such biomarkers are based on a probabilistic approach and
show the capability to discriminate between well-performed exercises and incorrect movements. A long-term experimentation on a specific exercise is
proposed, showing a considerable improvement, ranging from 5\% to 30\%, in
the performance of patients.
Keywords: rehabilitation; virtual reality; motor inabilities; visual tracking;
An Evaluation of Released Mobile Health Apps in Popular Iranian App Stores
by Hamid Naderi, Kobra Etminani
Abstract: The rapid growth in development and using mobile health applications had significant potential in the field of health. The study objective was to describe apps in the most popular Iranian Android and IOS app stores. Detail information of apps were extracted, categorized by subject and descriptive analyses were done. 3331 android and 277 IOS apps were remained for study. 88% of popular android mHealth apps were free on the other hand 70% of popular IOS apps were paid. The average price of android and IOS paid apps were $0.37 and $6.55. The average of rating value for all android subcategories (total average 4.33) is higher than IOS subcategories (total average 3.58). Our study confirmed fitness was the most popular topic. The average of rating value for all android subcategories was higher than IOS subcategories. It seems large number of mHealth app users preferred using applications which can update their content continuously.
Keywords: mHealth; Iranian app stores; Mobile applications; Descriptive analysis.
Digital Watermarking in Medical Imaging: A Review
by Hana Ouazzane, Hela Mahersia, Kamel Hamrouni
Abstract: This paper presents considerations on the digital watermarking potential to fit within the medical imaging field. We aim to identify watermarking applications and main strategies within different approaches through previous works. An analysis and evaluation of previous literature quality is also established. We conclude that digital watermarking has considerable purposes in the medical imaging field, it joins and complements existing security measures and management tools for medical images.
Keywords: digital watermarking; fragile/semi-fragile watermarking; robust watermarking; lossless watermarking; medical imaging; content security; e-health; medical information systems.
An Innovative Solution for prevention of reverse flow of blood in IV set (Intravenous set) using Embedded Systems
by Abhishek SN, Ikram Shah, K.V. Shriram
Abstract: The demand for medicine and doctors is never ending and it is really evergreen as our society has been revolutionized and the life expectancy rate has increased. Everyone starting from a six year old child to 90 year old veteran is aware of all kind of diseases exists as of now, specialization of doctors, which doctor to approach when we have a particular problem and so on. Thus, a common wish prevailing in everyone irrespective of age is that to live long and stay in pink of health. Adding to this point, millions and millions of innovations are being made in the medicinal field every decade to enhance the average life expectancy rate. There are various sectors of engineering that are involved in creating and/or maintaining various medical instruments. Say, from the smallest equipment like syringe to the largest one like CT scan machine have major contributions from the engineering sectors. IV Set (Intravenous set) is one among those substantial inventions in the medicinal field; it is actually used for transferring blood or saline from the IV/fluid bag to the patients body at a speed and pressure that the human body can accept. There is a notable threat associated with using the IV set which most of the doctors and care takers are aware of; When the fluid in the IV bag runs out (gets emptied) the patients blood would start flowing through the IV tube in the reverse direction towards the empty IV bag as the pressure of blood in the human body is more than the pressure experienced by the empty IV bag (diffusion), thus leading to blood loss and pain, at times. Thus the caretakers must be vigilant enough to replace the fluid bag at an appropriate time to avoid blood loss. The innovation proposed here is a solution that would prevent such blood losses and doesnt require manual monitoring of the IV bag thereby stopping the reverse blood flow well in time. This not only prevents blood loss but also reduces the stress for the care takers and pain for the patients. The product is tested and is costing less than 100 USD and is one-time investment for the hospitals.
Keywords: Intravenous set; Blood loss; intelligent IV Set; Hospitals; Medicine; Saline; Intelligent medical support; Patient monitor;.
Identification of Important Biomarkers for Detection of Chronic Kidney Disease using Feature Selection and Classification Algorithms
by E. Sivasankar, Pradeep R, Sivanandham S
Abstract: Health informatics plays a critical role to discover, innovate and reform the healthcare system towards betterment by fostering a collaboration between patients' data and medical practices. Development of easy, accurate and convenient methods for detection of diseases is a growing field of study in health informatics because of its easy scalability and affordability. In this paper, we have tried to identify important biomarkers for the detection of Chronic Kidney Diseases. We have used filter and wrapper based feature selection techniques and four different classification methods to analyse and interpret data to help us diagnose Chronic Kidney Disease faster. We have reported the top attributes and their predictive accuracy in detecting CKD and Support Vector Machine with Chi-squared feature selection method provided us with the best accuracy.
Keywords: Chronic kidney disease; CKD; Feature selection; Classification; Detection predictive model; Biomarkers for CKD; Risk factors; Machine learning; glomerular filtration rate; GFR; Nephrology.
Discrimination of signals phonocardiograms by using SNR report
by Mostefa Meriem, Hamza Cherif Lotfi, Debbal Sidi Mohammed El Amine
Abstract: The phonocardiographic PCG signal presents the acoustic recording of heart sounds; witch may reveal significant information that the human ear cannot. The current study develops methods for classification of signals using wavelet transform PCG, in Particular, the discrete wavelet transforms (DWT). In every case of PCG signal, different degree of noise will be gradually added and Ratio Signal to Noise (SNR) is Calculated. The intensity of the (SNR) is the basic parameter used in the classification of signals PCG.
Keywords: Phonocardiogram; Discrete wavelet transforms; SNR; Sound; ER.
An inventive and innovative system to detect fall of old aged persons A novel attempt with IoT, Sensors and Data Analytics to prevent the post-fall effects.
by Juluru Anudeep, Gudimetla Kowshik, Popuri Vamsi Krishna, Ikram Shah, K.V. Shriram
Abstract: According to the statistics of NCOA (National Council on Aging) these days rate of deaths in the old aged people has reached a critical state that for every 11 seconds an older adult is being treated for a fall and for every 19 minutes a death is taking place. Most of the old aged people are suffering from the back pain, joint pain, knee pain and they mostly are the ones who arent able to walk properly and are mostly constrained to bed. Statistics by north eastern Ohio state reveals that, the rate of emergency room treatment of fall-related injuries in persons aged 75 and older approached 80 per 1,000 per year in women and 60 per 1,000 per year in men. Falls are indications to some critical problems like immobility, fragility of bones and chronic health impairment. Falls in elders vary from that of the falls in kids, as the healing power in the old people slackens with their age. Falls are going to play a crucial role in the health line of the old people. Due to the falls there will even be a huge impact on the heart. Old people with heart diseases when falls suddenly, there is risk of being affected by Tachyarrhythmia which is typically a heart disorder in which the heartbeat raises to an abnormal rate of more than 100 beats per minute which is approximal to an cardiac arrest. So, in order to reduce these type of risks in elderly people , We present a frugal and affordable system that could monitor the motion of the old people and can detect their fall. Detection is not done before the fall or when person remains in rest state, it detects immediately after fall and alerts the concerned persons to do the necessary action to save the person. We use IoT, Sensors and Data analytics to build this system. The system is tested for its functioning and is fool proof.
Keywords: Fall detection; Old age support; IoT; IoT for medicine; Old aged tracking system; Gyroscope;.
Pragmatic Realities on Brain Imaging Techniques and Image Fusion for Alzheimers Disease
by P.S.Jagadeesh Kumar, Yang Yung, Mingmin Pan
Abstract: Alzheimers Disease is the absolute generous of age-related neurodegenerative syndrome. It specifies substantial brain atrophy, amnesia and major neuro-logic disintegration. Brain imaging techniques has been progressively employed for medical explanation and variant diagnosis, and to afford understanding into the paraphernalia on functional and structural measurements of the brain, sketches of dimensional distribution besides their normal antiquity and progression over time. This paper makes an exertion in performing a practical virtuosity on brain imaging techniques and image fusion for the diagnosis of alzheimers Disease. Several brain imaging techniques like computerized tomography, single-photon emission computed tomography, magnetic resonance spectroscopy, positron emission tomography, magnetoencephalography, magnetic resonance imaging diffusion tensor imaging was evaluated for alzheimers disease based on their degree of confidence, quality, volumetry, availability, cost and limitations.
Keywords: Alzheimer’s Disease; Brain Imaging Techniques; Image Fusion; Performancer Evaluation; Neurodegenerative Syndrome.
An Artificial Intelligence approach for the recognition of early stages of - ECZEMA
by Dhananjay Kalbande, Rohit Naik, Janvi Jatakia, Uday Khopkar
Abstract: The rural population of India currently suffers from various medical
ailments and due to the lack of medical facilities and practitioners, enough
support is not available. Medical help might come late and in the meanwhile,
the problem might have been aggravated. In usual cases, it might not be possible
to provide high-quality medical help, but nowadays, with the world advancing
technologically, almost everything seems within reach. With the increasing
awareness about artificial intelligence, mobile computing, and the trend of image
classification, it is now possible to solve these problems using technology.
The research aims at detecting an early stage of the skin disease Eczema in
individuals, when the affected part of the human body is captured as an image
through a smart phone device and approximate symptoms are provided by the
medical practitioner. It uses Artificial Intelligence algorithms like Convolutional
Neural Networks and Support Vector Machines Algorithms for classifying the
images, and Back Propagation algorithm for training a model based on the
symptoms. Around 50 Clinical photographs of Eczema acquired from Department
of Skin andV.D of Seth G.S.M.C&KEMHospital, Mumbai were used to train the
classifier and then different images were tested for the disease with an accuracy
of greater than 85%.
Keywords: Artificial Intelligence; Backpropagation Network; ConvolutionalrnNeural Network; Support Vector Machine.
Transmission and Archiving of Reduced MRI Medical Images
by Hedi AMRI, Med Karim ABDMOULEH, Ali KHALFALLAH, Jean-Christophe LAPAYRE, Med Salim BOUHLEL
Abstract: Medical images are often large. To minimize their file sizes, different compression standards like JPEG, JPEG 2000 and TIFF have been developed, but they can affect the image quality in case of lossy compression. In this paper, we propose a compression method based on image resizing that can reduce the file size to a quarter before its transmission or archiving. When it is received or before its display, the reduced image is enlarged to ensure better visual comfort. This approach is called REPro. In this context, we have used three image reduction techniques namely Square-Square Mesh Decimation, Square-Square filtered Mesh Decimation, the Square-Staggered-Square filtered Mesh Decimation. In addition, we have utilized four enlargement techniques namely Zero padding, Nearest Neighbor interpolation, Cubic Spline interpolation and B-interpolation to resize MRI images. The experimental results prove that the combination of the Square-Square Mesh Decimation with the B-Spline interpolation ensures the minimum distortion of the original image.
Keywords: Telemedicine; Archiving; Transmission; Medical images; image Reduction; image Expansion.
Breast Cancer Detection in Mammogram image with Segmentation of Tumor Region
by Vikramathithan A C, Dandinashivara Revanna Shashikumar
Abstract: In our proposed breast cancer malignant detection study are performed with the aid of fuzzy min max neural network technique. Majority of womens are affected in this breast cancer at a early stage the mammogram images are mostly play in a vital role. Initially the input mammogram image smoothened with the aid of adaptive median filer from that smoothened image we are segmenting tissues with the aid of Histon based Fuzzy c-means clustering. We are extracting features from that segmented image the features are statistical and semantic features. Then we can identify the malignant region with the aid of these features. The segmented region is maligned or benign using an optimal fuzzy min max neural network with gray wolf optimization algorithm with the aid of these we will identify a breast cancer region.
Keywords: Breast Cancer; Mammogram; Fuzzy min max; gray wolf; optimization; segmentation.
Radial Basis Function Neural Network with Genetic Algorithm for Discrimination of Recombination Hotspots in Saccharomyces Cerevisiae
by Ashok Dwivedi
Abstract: Recombination influences the evolution of Saccharomyces Cerevisiae. Genomic regions where
recombinations occurs are known as recombination hotspots. There are two kind of hotspots for recombination. The spots where recombination occurs more frequently are called recombination hot spots and regions where recombination occurs less frequently are known as cold spots. In this work, we have formulated methods based on neural network models for the classification of these hot and cold recombination spots on the basis of compositional features of nucleotide sequences. These models were validated using tenfold cross validation technique. The classification accuracy of 83%, 82%, and 78 % were achieved using radial basis function neural network with genetic algorithm, radial basis function neural network and multilayer perceptorn models respectively. Moreover, the performance of these model were evaluated on differenct classification measurements. Furthermore, results indicate that redial basis function neural network with genetic algorithm gives best result.
Keywords: : Artificial Neural Network; Radial Basis Function Neural Network; Genetic Algorithm; Multi-Layer Perceptron; Classification; Machine Learning; Evolution.
Analysis of salivary components as non-invasive biomarkers for monitoring chronic kidney disease
by Navaneeth Bhaskar, Suchetha M
Abstract: Saliva, a valuable source of biochemical information, is a potential diagnostic substance that helps to identify many diseases. Studies have revealed that saliva tests help identify many diseases. Saliva test has excellent advantages over blood test as the former can be collected non-invasively using simple equipment. This paper explores how salivary components can be used as diagnostic tool to identify chronic kidney disease (CKD). Experimental analysis was conducted to assess the levels of salivary components in whole saliva of CKD patients in contrast with healthy people. Urea and creatinine are the most accepted biomarkers of CKD. The correlation between creatinine and urea levels in human saliva and blood were analyzed. Unstimulated saliva flow rate and pH levels were also monitored in this study. The results obtained from this study give concrete evidence that there is a positive correlation between creatinine and urea levels in blood and saliva. From the derived regression line equations, serum urea and creatinine values can be predicted from salivary values. Receiver operating characteristics (ROC) performance analysis was performed and area under the curve (AUC) of 0.95 and 0.89 was obtained for salivary creatinine and urea.
Keywords: saliva; non-invasive; electrolytes; urea; creatinine; chronic kidney disease.
Time-Frequency Analysis based Method for Application of Infant Cry Classification
by J. Saraswathy, M. Hariharan, Wan Khairunizam, J. Sarojini, Sazali Yaacob
Abstract: This paper proposes a new investigation of time-frequency (t-f) based signal processing technique using wavelet packet spectrum (wpspectrum) for classification of newborn cry signals. T-f approaches can analyze the non stationary signals comprehensively in joint t-f domain compared to the time or frequency domain techniques. This study was initialized with the extraction of a cluster of t-f features from the generated t-f matrix of recorded cry signals using wpspectrum by extending time-domain and frequency-domain features to the joint (t-f) domain. In accordance, conventional features such as Mel-frequency cepstral coefficients (MFCCs) and Linear prediction coefficients (LPCs) were also extracted in order to compare the performance of the suggested t-f approach. Probabilistic neural network (PNN) and general regression neural network (GRNN) were used to evaluate the efficacy of the extracted feature vectors. The proposed methodology was implemented to classify different sets of infant cry signals cry including binary and multiclass problems. Best empirical result of above 99 % was reported and revealed the good potential of t-f methods in context of infant cry classification.
Keywords: Keywords: Infant cry; Time-frequency analysis; Feature extraction; Classification.
Improving ECG Signal Denoising using Wavelet Transform for the Prediction of Malignant Arrhythmias
by Agostino Giorgio, Cataldo Guaragnella, Domenico Andrea Giliberti
Abstract: Objective: This paper deals with the accuracy of algorithms for the detection of cardiac Ventricular Late Potentials (VLP). The presence of VLP in an electrocardiographic signal (ECG) is associated with possible sudden cardiac death, because of malignant arrhythmias. VLP detection is strongly influenced by signal noise, thus the ECG needs to be denoised before VLP detection. The objective of this paper is to define a denoising algorithm improving the VLPs detection in ECG signal, and to describe its hardware implementation on a Field Programmable Gate Array device (FPGA). Methods: The method described uses wavelet denoising, implemented as subband coding. The drawbacks of this method are heavy linear distortions undergone by the analyzed signal. This disadvantage is overcome by using an equalization filter properly designed by the authors for canceling the introduced distortions. Results: The algorithm (equalizer filter + wavelet denoising) has been firstly implemented and successfully verified using MATLAB. Then, it has been implemented as programmable hardware on Alteras FPGA. The synthesized hardware has been verified on the evaluation board DE1-SoC, mounting a Cyclone V 5CSEMA5F31C6 FPGA chip. Conclusions: On board processed results and theoretical results are consistent, validating the effectiveness of the algorithm and of the designed hardware. Significance: Results show that the algorithm accuracy and its capability to be implemented as programmable hardware also could be used for upgrading ECG devices reliability in the field of heart diseases prevention.
Keywords: Wavelet Transforms; Signal Detection; Biomedical Electronics; Denoising; FPGA.
Malignant Melanoma Detection using Multi Layer Preceptron with Visually Imperceptible Features and PCA Components from MED-NODE Dataset
by Soumen Mukherjee, Arunabha Adhikari, Madhusudan Roy
Abstract: In this paper, a scheme is worked out for classification of images belonging to malignant melanoma and nevus class by multi layer neural network architecture with different trainings and cost functions. Total 1875 shape, colour and texture features are extracted from 170 images from MED-NODE dataset. With the total 1875 features an accuracy of 82.05% is achieved. Feature ranking algorithm ReliefF is used for ranking these features. MLP is run with varying number of best ranked features. With 10 best features an accuracy of 83.33%, sensitivity of 86.77% and specificity of 72.78% are achieved with 3 fold cross-validation. Effect of pre-processing the features with Principal Component Analysis is explored and found that the optimal number of principal components is 25, which yields a maximum accuracy of 87.18% which is much higher than the previously reported accuracy level with this dataset.
Keywords: ABCD rule; GLCM; GLRLM; ReliefF; Principal Component Analysis; Autocorrelation; Cross Entropy; t-test.
A REVIEW ON AUTOMATIC IDENTIFICATION OF FOVEA IN RETINAL FUNDUS IMAGES
by Rajesh I S, Bharathi M A, Bharati M. Reshmi
Abstract: Identification of retinal diseases is a very significant area of ophthalmology. Regular procedures are extremely specific, which rely on manual observation and highly prone to error. Hence, it is extremely fundamental to set up an automatic system for screening of vision-threatening diseases like Diabetic Retinopathy (DR) and Diabetic Maculopathy (DM). Patients who are suffering from DR are at high risk to have DM which may lead to blindness, if not detected and treated appropriately at the appropriate time. Automatic analysis of retinal images requires knowledge and the properties of anatomical structures and retinal lesions. Thus, locating fovea plays a vital role in the analysis of retinal images. In recent times image processing has become a very effective tool for the detection and analysis of abnormalities in retinal images. This survey paper depicts the fundamental terminology related to automatic detection of macula and fovea. The literature review of various methods used for finding fovea in retinal fundus images is discussed. Detection issues involved in fovea are also discussed in this paper.
Keywords: Diabetic Retinopathy (DR); Diabetic Maculopathy (DM); Optic Disc (OD); Region of Interest (ROI); Blood Vessels (BVs).
Enhancement and segmentation of histopathological images of cancer using dynamic stochastic resonance
by Anuranjeeta Anuranjeeta, Munendra Singh, K.K. Shukla, Neeraj Sharma, Shiru Sharma
Abstract: Pathologists face difficulty in cell image detection as uneven dye causes the low contrast and inhomogeneity. The proposed Discrete Cosine Transform (DCT) based Dynamic Stochastic Resonance (DSR) technique enhances the histopathological images of cancer. Further, the DSR based Otsus thresholding processed image helps in the better segmentation of histopathological images of four types of cancer cells, i.e. breast, cervix, ovarian and prostate cancer. The comparison of segmentation results were performed on the University of California, Santabarbara (UCSB) available breast cancer datasets for analysis. The algorithm has been applied to total twenty-two (22) breast cancer images including benign and malignant and compared with Region of Interest (ROI) segmented Ground Truth images to validate the performance of proposed DSR based Otsus thresholding. DSR based Otsus segmentation obtained better results with 0.776 average correlation, 0.979 average normalized probabilistic rand (NPR) index, 0.011 average global consistency error (GCE), and 0.185 average variation of information (VI). These indices are higher than the other conventional segmentation methods and have the advantage to identify the target objects in low contrast images.
Keywords: dynamic stochastic resonance; image enhancement; tissue; segmentation; histopathological image.
A Decision Support Tool Development: An Analysis of the Statistical Significance of the Dichotic Listening of Speech Test Results
by Elena A. Popova, Evgeny L. Wasserman, Nikolay K. Kartashev
Abstract: In this study, we analyze the statistical significance of the dichotic listening test results using two methods. The first method is based on the Agresti-Coull interval, and the other one is based on the sequential Wald analysis. We also propose a way to estimate the guaranteed boundaries for the eventual laterality index at every step of the dichotic listening test using the random walk method. This work is based on the analysis of 87 dichotic listening protocols of 59 children and adolescents of 416 years of age (patients of children's psychoneurological clinic) and three healthy adult volunteers. The dichotic listening was carried out in the clinical and laboratory conditions.
Keywords: dichotic listening; speech; laterality index; random walk; lateralization; sequential Wald analysis; Agresti-Coull interval; decision support tool; statistical significance.
A Semantic Interoperability Framework for Distributed Electronic Health Record Based on Fuzzy Ontology
by Mohammed Elmogy, Ebtsam Adel, Shaker El-Sappagh, Sherif Barakat
Abstract: To achieve an efficient healthcare process; the professionals and doctors need to access the complete data about their patients in the suitable time. In the face of that issue; the medical data by natural is distributed across different systems and heterogeneous sources. From another angle; Healthcare semantic interoperability is still a huge problem without solving. In this paper, a unified semantic interoperability framework for distributed EHR based on fuzzy ontology is proposed. It consists of three main layers. The lowest layer (local ontologies construction) stores the EHRs heterogeneous data with different database schemas, standards, terminologies, purposes, locations, and formats. In the middle layer (global ontology construction) the local ontologies are mapped (using mapping algorithms or human experts with the help of common terminology vocabularies) to a global crisp one. The global reference ontology combines and integrates all local ontologies and therefore describes all data. Finally, the third layer is the user interface, in which a doctor can ask any linguistic or semantic queries by dealing with only the global reference fuzzy ontology. We expect that our framework will handle the current EHR semantic interoperability challenges, reduce the cost of the integration process, and get a higher acceptance and accuracy rate than previous studies.
Keywords: Semantic Interoperability; Fuzzy Ontology; Unified data model; DB2OWL; X2OWL.
Changes in scale-invariance property of electrocardiogram as a predictor of hypertension
by HELEN MARY M C, Dilbag Singh, K.K. Deepak
Abstract: In this study, electrocardiogram signal has been investigated to assess the presence of scale-invariance changes to classify normotensive and hypertensive subject. ECG signal of 20 normotensive and 20 hypertensive subjects is recorded using MP100 system with a sampling rate (f_s) of 500 Hz. The scale-invariance changes of ECG signal are analyzed using multifractal detrended fluctuation analysis. The width and shape of the multifractal spectrum obtained is used to detect the hypertensive subject. The multifractal spectrum of the normotensive subject exhibit a pure Gaussian behavior, but for hypertension subject is left truncated. The width of the multifractal spectrum for hypertension subject (1.7158
Keywords: Scale-invariance; Electrocardiogram; Multifractal spectrum; Hypertension.
Modified Model for Cancer Treatment
by Mahdi Rezapour Shafigh
Abstract: This paper proposes an optimal method for eradicating cancer, such that it cannot be relapsed. The major issue is that from the dynamical point of view, the tumor free equilibrium point at the end of chemotherapy is still unstable. Mathematically it means that when the chemotherapy is stoped, the dynamic behavior of the system moves away from the tumor free equilibrium point and the tumor cells starts increasing. To overcome this problem, we can either restart the process of chemotherapy or we try to stabilize the equilibrium point. In this paper the stabilization of the equilibrium point is proposed by applying a combination of vaccine therapy and chemotherapy method. According to this method, the vaccine therapy changes the dynamics of the system around the tumor free equilibrium point, and the chemotherapy pushes the system to the domain of attraction of the desired point. A suboptimal control strategy based on the State Dependent Riccati Equation (SDRE) is used for the chemotherapy process in this nonlinear cancer model. According to this method it is possible to study the particular conditions of an individual patient by choosing proper weighting matrices in the cost functions and limiting the dose of chemotherapy drug. It is shown, that, according to the simulation results, after completing the chemotherapy process, the dynamic of the system becomes stable and the cancerous cells converges to zero.
Keywords: Cancer, Chemotherapy, Lyapunov stability, Mathematical model, SDRE control, Vaccine therapy
Special Issue on: Health Engineering and Informatics
Bio-Medical Analysis of Breast Cancer Risk Detection based on Deep Neural Network
by Nivaashini M, Soundariya R S
Abstract: - Breast tumor remains a most important reason of cancer fatality among women globally and the pathetic condition is that most of them pass away due to delayed diagnosis. But premature recognition and anticipation can significantly diminish the chances of death. Risk detection of breast cancer is one of the major research areas in bioinformatics. Bioinformatics can be narrowly defined as a field at the crossroads of biology and computer engineering, responsible for the storage, distribution, and analysis of biological information. Various experiments have been conceded to examine the fundamental grounds of breast tumor. Alternatively, it has already been verified that early diagnosis of tumor can give the longer survival chance to a patient. Consequently, early prediction of breast tumor is very critical to rescue a patients life. To deal with this issue, in the proposed framework a model with deep learning techniques has been introduced to identify breast tumor at primary stage. This paper aims at finding an efficient set of features for breast tumor prediction using deep learning approaches called Restricted Boltzmann Machine (RBM). The proposed framework diagnoses and analyzes breast tumor patients data with the help of Deep Neural Network (DNN) classifier using the Wisconsin dataset of UCI machine learning Repository. and, thereafter assesses their performance in terms of measures like Accuracy, Precision, Recall, F-Measure, etc.
Keywords: Breast tumor Prediction; Feature Selection; Restricted Boltzmann Machine; Deep Neural Network.
A REVIEW ON EMOTION RECOGNITION IN PARKINSONS DISEASE USING BIOINFORMATICS
by Rejith K.N., Kamalraj Subramaniam
Abstract: Parkinsons disease individuals have been stressed and shown difficulty in emotion recognition & facial expression with increasing cognitive decline. In recent years, various studies have been conducted in emotion recognition of Parkinsons disease patients. In many research works, emotional state assessment using facial expression and EEG based stimuli were used for emotion recognition study. In this paper, a review of EEG based emotion recognition in Parkinsons disease and its various research analyses in the past two decades have been analyzed. Most of the papers have investigated total of six emotions in Parkinsons disease study such as happiness, sadness, fear, anger, surprise, and disgust to evaluate the emotion difficulties in Parkinsons disease people.
Keywords: Cognitive deficit;Electroencephalogram;Emotion; Emotional deficits; Event related potencial;Facial emotion recognition; Non-linear methods; Parkinson’s disease.
QUERY OPTIMIZATION WITH WEIGHTED FISH SCHOOL SEARCH IN ONTOLOGICAL DATABASE WITH APPLICATION OF BOINFORMATICS
by Jaya Raju
Abstract: Making queries from large ontological database has a severe problem of generating query plans as it is made in form of left tree search form. This restricts the querying for composite applications and speed of acquiring query results. In such a scenario the most prominent approach is to optimize the indexing of graph nodes in ontological database and many evolutionary and particle of swarm optimization (PSO) based approach had already been attempted. However, loss of diversity and unanticipated convergence causes the solution to remain sub-optimal. In this study we present a weighted fish school searching based query optimization technique owing to its scalability and self control functioning with the application of bioinformatics. It creates probabilistic logic based weight system for the fish school search in a hierarchical tree form which results in increased accuracy when put in comparison with standard PSO based methods and its other variants
Keywords: optimization; semantic data; PSO.
A cloud based secured Framework for smart Medical Diagnosis: A Survey
by Leelavathy Mrs, Selva Brunda S
Abstract: Enormous amounts of medical data are being collected by several well-developed HIS (Hospital Information Systems) in the form of patient records in hospitals. The hidden patterns and relationships contained in this data are identified using various data mining techniques which has drawn increasing world-wide attention in the recent years. As a result of which, a good number of MDSS (Medical Decision Support Systems) have been developed. These are computer systems designed to assist physicians or other healthcare professionals in making clinical decisions for the given patients symptoms and medical history. Inspired by the existing systems, the model proposed in this paper aims at making a knowledge sharing collaborative platform for doctors which serves as a realistic and effective Medical Decision Support System. It is important because it provides vital information from different data sources. It has several challenges like scalability, response time , heterogeneous data formats . This paper does a detailed study of different challenges in understanding about medical information that can be provided to users in a better manner . A framework named Intelligent Health care Framework(ISHF) is being proposed in this paper.
Keywords: Medical decision support system (MDSS); Bioinformatics; diagnosis; knowledge discovery and symptoms.
A Study on Indirect Immunofluorescence Image Classification Methods for Bioinformatics
by Divya BS, Kamalraj Subramaniam, Nanjundaswamy HR
Abstract: The Indirect Immunofluorescence (IIF) test with Human Epithelial type-2 (HEp-2) cells as substrates is the gold standard for Anti Nuclear Antibodies (ANA) test to diagnose autoimmune diseases. The specialists in the laboratory visually examine the specimen under microscope to recognize the staining patterns and generate the report. So ANA test is subjective and needs systemic automation for bioinformatics. In this view international benchmarking initiatives were organized by IAPR in the last six years. In this paper the state of the art on IIF HEp-2 cells classification task was analyzed. This paper highlighted the original aspects with the detailed discussion of the published methods. Design choice verses performance was analyzed.
Keywords: Anti Nuclear Antibodies; pattern classification; HEp-2 cell,Computer Aided Diagnosis; Indirect Immunofluorescence; Healthcare System.
Special Issue on: Biomedicine in Industry and Society
ALLIUM SATIVUM - BREAST CANCER THERAPEUTIC AGENT TO REPLACE ALLOPATHIC TREATMENTS WITH EXTENSIVE SIDE - EFFECTS
by Neha Vutakuri, Sita Somara
Abstract: Cancer is a life-threatening disease that can grow in any part of the human body and may result in the abnormal growth of millions to trillions of cells. There are currently 200 types of human cancers. Cancer is treated with various treatments, such as surgery, radiation therapy, chemotherapy, immunotherapy, stem cell transplantation, targeted therapy, and others. However, these treatments produce side effects, such as anemia, diarrhea, appetite loss, bleeding, hair loss, infections, lymphedema, nerve problems, urinary, bladder problems, and others. The side effects of cancer treatments may also lead to death. Some spices are used to naturally fight cancer. Spices are usually used in cooking as a flavoring ingredient and may have thousands of medicinal characteristics. Spices such as turmeric, ginger, garlic, saffron, black pepper, cayenne pepper, mustard, mint leaves, black cumin, and others are used in Ayurveda medicines for the natural treatment of cancer. This paper addresses the use of garlic (Allium sativum) in the treatment of breast cancer and its promising results. Garlic is a medicinal plant and an antibacterial agent that provides many benefits to humans if consumed in the regular diet. Garlic has been used for thousands of years to treat several diseases and is known to inhibit breast cancer growth. Many epidemiological studies have found that garlic is one a natural remedy that inhibits the growth of breast cancer cells. The dietary consumption of garlic in various forms effectively minimizes the growth of breast cancer and other diseases. This paper provides a complete description of garlic including its history and then discusses its utility for preventing of breast cancer and its use in clinical trials; hence, this paper will be helpful for researchers and physicians in treating breast cancer.
Keywords: Breast cancer; Spices treatment; Garlic; Ayurveda; Antibacterial agent.
Investigation of Problems faced during capturing of Gait Signals
by Vinothkanna Rajendran, Prabakaran Narayanaswamy, Sivakannan Sivakannan
Abstract: To solve the issue of close human contact in biometric authentication system, relatively a new technique gait recognition is used. The human gait is a common feature for identifying the walking manner of the person during walking and it is viewed as significant indicator for gait function of individual for experimental and research setting. Gait symmetry is usually considered as function of locomotion between the changes of the body and its activities. An exclusive advantage of gait as a biometric is its latent for detection at a distance or at low resolution or when other biometrics might not be perceivable. In this paper we investigate the problem of people recognition by their gait. The coordination and cyclic nature of the body motion makes gait as unique characteristics of each individual, thus a good biometric identification approach. The purpose of this paper is to describe some of the problems that make it difficult to apply gait as a biometric identification and use recent literature to show suggestions being made to solve some of these technical issues.
Keywords: Gait recognition; biometrics; walking style; pathology; injuries.
Improving the Classifier accuracy with an Integrated approach using Medical data - A Study
by Maragatham Ganesan, Rajendran S
Abstract: As information plays a vital role in the current scenario, Fetching of information from the voluminous quantity seems to be challenging. Therefore, the data mining community work on this area to find an improved solutions to help the end users. The end users may be an organization or may be an ordinary user. The authors have used different classification techniques for the study purpose. The article attempts to analyze the accuracy of classifiers with respect to that of medical data. Dataset from the repository is considered for analyzing purpose. Initially a preprocessing step is used on the data set for finding out the missing values. Next, the resulting data set is applied to the classifiers to study its performance accuracy. Initially, after the preprocessing step a classifier is selected based on which the accuracy of the classifier is studied. Next, a subset evaluation step is performed to determine the relevant attributes. In order to improve the classifier accuracy an attribute selection filter of supervised category is selected. In this article, the authors have done a study on integrated approaches that helps in classifying the instances of a bio-medical data. For the analysis purpose the Na
Keywords: Naive Bayes; Multilayer perceptron; chi-square; logistic regression.
Wavelet Packet Transform based Medical Image Multiple Watermarking with Independent Component Analysis Extraction
by Nanmaran Rajendiran, Thirugnanam Gurunathan, Mangaiyarkarasi Palanivel
Abstract: Rapid growth of internet in all aspects of life has led to the easy availability of the digital data to everyone. E-commerce, telemedicine etc., are among the many applications of internet. Telemedicine is a crucial field where internet finds application. Healthcare professionals use internet to transmit and receive medical data. Thus the medical images can be shared, processed and transmitted through computer networks. All patient records, linked to the medical secrecy, must be confidential. Because of the importance of the security issues in the management of medical information, there is a need to develop watermarking techniques for protecting medical images. In this paper, colour medical image watermarking methods rely on Wavelet Packet Transform (WPT) and extraction using Independent Component Analysis (ICA). For watermark extraction, Pearson ICA is applied as it attains the new trait is that it not entail the renovation procedure in watermark extraction. The grades show that projected method is vigorous beside attacks such as Gaussian noise, Salt and Pepper noise, Rotation and Translation. The performance measures like PSNR, Similarity Measure and Normalized Correlation are assessed to confirm the robustness of the scheme.
Keywords: discrete wavelet transform; image watermarking; independent component analysis; pearson ICA; wavelet packet transform.rn.
Novel feature extraction of EEG signal for accurate event detection
by S. Saravanan, S. Govindarajan
Abstract: Electroencephalograph (EEG) signal analysis is one of the essential for diagnosis of diseases, detect the various event and psychological problems. The accuracy of event detection depends on the feature vectors used. The feature vectors in literature provide performance only for a specific application and perform poor for other application. This paper presents a new feature vector generation using fusion of energy feature vectors of different types. The energy features of alpha, beta and gamma component is extracted as base feature. A fusion rule is formulated to fuse that base features using Hjorth activity features to improve the accuracy of classification. The performance of the proposed new feature is tested on seizures detection, sleep state detection and emotion detection application. The resulting analysis shows that the proposed new feature outperform with improved accuracy of 26% for emotion detection, 5% for seizures detection and 4% for sleep state detection.
Keywords: electroencephalograph; EEG; event detection; energy feature; emotion detection; feature extraction; fusion; Hjorth activity; seizures detection; sleep state detection.
An Amalgamated Prediction Model for Breast Cancer Detection using Fuzzy Features
by Smita Jhajharia
Abstract: Machine learning techniques for cancer prognosis are being widely researched and applied. Supervised learning in conjunction with other computational techniques are paving the way forward for predictive health analytics. The step of input feature processing is very important in order to obtain meaningful results from a data analytics problem. In this paper, the Extended Kalman Filter (EKF) and Fuzzy K-Means clustering algorithms have been combined and a hybrid algorithm is proposed with improved functionality compared to either of the two separately. The proposed hybrid algorithm implements a fuzzy K-means algorithm with Support Vector Machine (SVM)coupled with an EKF for data filtering. From the filtering and prediction consecutive cycles, the result of Kalman filters is obtained and fuzzy membership functions are calculated in order to form a relationship between the labels and attributes. K-means utilizes this relationship to create a new modified set of attributes, which are given to the SVM classifier, with lesser number of support vectors. The number of clusters is added into the training process as the input parameter except the kernel parameters and the SVM penalty factor. The approach was tested for various publicly available data sets like UCL,SEER and a real data set compiled by the authors. After performing statistical analysis, the accuracy, precision, recall and F-score value of the algorithm have been found and compared against those obtained from the traditional algorithms.
Keywords: Cancer; Clustering; Extended Kalman Filter; Fuzzy K-Means.
OPTIMIZED FEATURE SELECTION AND ENTROPY-BASED GRAPH CLASSIFICATION OF GENE EXPRESSION DATA
by Audu Musa Mabu, Rajesh Prasad, Raghav Yadav
Abstract: Purpose: Gene expression (GE) profiles expansively revised to disclose intuition into the multifariousness of cancer furthermore to discover concealed information which provides biological knowledge for the classification of cancer. Precise cancer classification straightly through original GE profiles stays challenging on account of the intrinsic high-dimension feature along with the small magnitude of the data samples. Therefore, choosing high discriminative genes as of the GE data have turn into progressively fascinating in the bioinformatics field. This given paper gives a technique for the GE data classification utilizing entropy based graph classifier.
Methodology: Initially, the proposed technique evaluate the GE datas SNR (Signal to Noise Ratio) values, additionally, selects the relevant features using KH (Krill Herd) optimization process. The truth is that not all features are helpful for classification, and some redundant together with the irrelevant features might even serve as outlier. To dispose the outliers, feature reduction is done with the assist of Euclidean distance. Classification is made utilizing entropy based graph classifier.
Conclusion: The proposed process effectiveness contrasted with the existing method concerning classifications is established from the Experimental outcome.
Keywords: Gene Expression Profile; Bioinformatics; Entropy; Graph Classifier; Krill Herd Optimization; Signal to Noise ratio.
Novel Multiphase Contouring and Force Calculation Algorithm for ROI Detection and Calculation of Energy value in Multiple Scale and Orientation for Early Detection of Stages of Breast Cancer
by Varalatchoumy M, Ravishankar M
Abstract: A Novel Multiphase Contouring and Force calculation (MCFC) algorithm has been developed to perform segmentation and detect Region of interest (ROI) in a very efficient manner in mammographic images. Detected malignant tumors were further processed using a novel approach in order to identify the stages of breast cancer (stage I or II) precisely. The whole process starts with the Preprocessing phase that aids in enhancement and noise removal of the input mammographic images. Preprocessed image is segmented using the MCFC algorithm to detect the ROI. This algorithm has aided in achieving robust segmentation at very low computation time. Combination of wavelet and textural features were used to train the Artificial Neural Network to classify the detected ROI as normal, benign or malignant tumor. Stages of the detected malignant tumors are identified using a very novel approach of calculating the energy values in four different scales and six orientations for each scale. A total of 24 energy values are used to train the Artificial Neural Network to detect the stages of the malignant tumor in a very precise manner. Overall system performance has been tested on 45 real time patients mammographic images obtained from hospitals. Detection of malignant tumor and its stages have been verified by experts in medical field. The overall accuracy of the novel algorithm and the staging approach if 97% for MIAS database images and 90% for real time mammographic images
Keywords: Multiphase Contouring and Force calculation (MCFC); stages of breast cancer; wavelet and textural features; energy values.
EEG signal analysis and classification on P300 speller-based BCI performance in ALS patients
by Mridu Sahu, Shrish Verma, Naresh Kumar Nagwani, Sneha Shukla
Abstract: Objective: the objective of the presented work is to analyse the electroencephalography signal based on brain computer interface by using P300 speller for amyotrophic lateral sclerosis (ALS) patients and perform classification on extracted features to get accuracy. Analysis/methods: amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that involves the degeneration and death of nerve cell in the brain. It affects the process related to speech and loss of motor function in the patient. BCI technology is a communication solution for all amyotrophic lateral sclerosis (ALS) patients. The P300 speller included in the BNCI Horizon 2020 data is an application allows calculating the accuracy of classifier, which is necessary for the user to spell letters or sentences in a BCI speller paradigm. In this paper, we have extracted wavelet and power spectral density features. Association rule mining and ranking method is used for feature selection. For the classification, we have used multiple techniques and different classifiers and out of those, ten best techniques are selected based on their good performance. Finding: as a result, we get maximum 75% accuracy when we used random committee classifier.
Keywords: amyotrophic lateral sclerosis; ALS; brain computer interface; BCI; electroencephalography; EEG; P300 speller; power spectral density; PSD; wavelet; association rule mining; ARM.