International Journal of Biomedical Engineering and Technology (7 papers in press)

Regular Issues

• A Feature Transfer-Based Deep Neural Network for Wearable SSVEP-EEG Signal Classification  
  by Yongquan Xia, Ronglei Lu, Chunlai Yu, Duan Li, Jiaofen Nan, Keyun Li, Zhuo Zhang 
  Abstract: The steady-state visual evoked potential(SSVEP)brain-computer interface(BCI)has attracted widespread research interest owing to its multitarget recognition capacity, high accuracy, and efficient information transmission However, the recognition accuracy of wearable SSVEP-BCI systems remains limited To address this issue, this study proposes a feature transfer-based bidirectional long short-term memory(FTBi-LSTM) classification model, which incorporates variational mode decomposition(VMD)and wavelet hybrid denoising for signal preprocessing Within the framework of bidirectional signal processing, SSVEP signals and same-frequency reference signals are paired as input for the bidirectional sub-networks Deep features are extracted using a feature transfer approach to achieve classification Experimental results show that under a 0.5-second time window, the classification accuracies for dry and wet electrodes reached 44.71% and 68.23%, while under a 0.2-second time window, the information transfer rates(ITR)increased to 142.96 bits/min and 337.42 bits/min, respectively, demonstrating the effectiveness of the FTBi-LSTM model in wearable SSVEP-BCI systems.
  Keywords: Brain-computer interface; Steady-state visual evoked potential,Wearable devices; Feature transfer; LSTM.
  DOI: 10.1504/IJBET.2025.10073954
   
  
• A Statistical Shape Modelling Framework and Software for Predicting Skull and Muscle Networks from Head  
  by Tan-Nhu Nguyen, Phong-Phu Vo, Vi-Do Tran, Ngoc-Bich Le, Nu-Vuong Nguyen-Tran, Minh-Thuong Truong, Tien-Tuan Dao 
  Abstract: Real-time biomechanical head simulation is necessary for providing bio-feedbacks for facial paralysis grading. This process is challenging and needs enhancement in both dataset and personalising procedure. We introduced a statistical framework for dataset generation, skull prediction, and muscle strain computation. The head-to-skull shape relation was trained through their shape parameters. After a ten-fold cross-validation, the mean testing error was 1.86 mm with 6.17s
  Keywords: Head-to-skull prediction; Biomechanical head simulation; Statistical Shape Modeling; Facial Paralysis Grading; Facial Mimic Rehabilitation.
  DOI: 10.1504/IJBET.2025.10074988
   
  
• Sensitivity Optimisation of an Optical Pressure Sensor-Based Low-Cost Dynamic Pedograph for Improved Foot Pressure Analysis  
  by Jewel Haque, Ibrahim Al Imran, Khondkar Siddique-e-Rabbani 
  Abstract: Accurate measurement and analysis of dynamic foot pressure are crucial for preventing complications associated with peripheral neuropathy, particularly in individuals with diabetes. This study focuses on optimising the calibration and sensitivity of an optical pressure sensor-based dynamic pedograph, designed and made in Bangladesh, to enhance its accuracy in assessing foot pressure distribution. The system employs total internal reflection in a transparent glass slab; foot-applied pressure disrupts light propagation, producing scattered light that is captured as greyscale intensity by a camera positioned beneath. Calibration was performed using a custom four-pad platform, establishing a linear relationship between applied pressure and pixel intensity (100200 out of 255). Dedicated Java-based software enabled real-time analysis and precise correlation. Systematic tuning of camera parameters gamma 100, gain 0, contrast 0 enhanced sensitivity, linearity, and spatial uniformity, with spatial sensitivity variation of 8.2% and temporal variation of 1.92, indicating stable performance. The optimised pedograph generates high-resolution pressure maps, providing a cost-effective, reliable alternative to commercial systems, supporting improved plantar pressure assessment and diabetic foot care in clinical settings.
  Keywords: Optical Pressure Sensor; Dynamic Pedograph; Foot Pressure; pixel intensity; peripheral neuropathy.
  DOI: 10.1504/IJBET.2025.10075153
   
  
• Tetralet Attention Enabled Modified N-Adam Optimised Distributed Capsule Network for Lie Detection from Electroencephalogram Signals  
  by Anand Ashok Ingle, Jayant P. Mehare 
  Abstract: Lie detection using an Electroencephalogram signal (EEG) has immense attention.Finite Impulse Response Filterpreprocessing the input EEG signal from lie wave’s datasets and the frequency splitup the process. If a person lies signal strength increases, if exceeds a limit, a lie is detected. However, The convolutional methods produces robustness and false positive rates. The Tetralet attention-enabled modified N-Adam optimized Distributed capsule Network (Tet-MNDCNet) is proposed. A combination of Tetralet attention-enabled modified N-Adam optimized Distributed capsule Network and the zero-attention mechanism provides a distributed capsule network. The model needs to ensure accuracy and reliability. The novel tetralet attention focused on selective features. The Tet-MNDCNet model performance is robust due to the HarmoniQ spectrum from the pre-processed signal. N-Adam optimizer reduces the gradient descent problem and improves the model’s interpretability. The accuracy of the experimental proposed lie detection task of 97.35% for K-fold is 10.
  Keywords: Distributed Capsule network; Lie detection; deep learning; modified N-Adam; Electroencephalogram.
  DOI: 10.1504/IJBET.2025.10075718
   
  
• Patient-Specific Approach for Automated Epileptic Seizure State Detection based on Deep Learning  
  by Vibha Patel, Dharmendra Bhatti, Amit Ganatra 
  Abstract: Epilepsy is a chronic neurological disorder that occurs due to irregular brain activities. An automated approach to detect the epileptic seizure state from EEG recordings is highly desirable as the manual approach is exhausting, time-consuming, and error-prone. This work presents a hybrid 1D-CNN + Stacked-LSTM model for an end-to-end, patient-specific EEG-based epileptic seizure state detection. The proposed work was tested on two datasets: CHB-MIT scalp EEG dataset and Siena scalp EEG dataset. It achieved highest result of 97.07% accuracy, 97.80% sensitivity, 97.07% specificity, 0.0293 FPR, and 0.99 AUC values on CHB-MIT dataset and 97.83% accuracy, 98.75% sensitivity, 97.82% specificity, 0.0218 FPR, and 0.99 AUC values on Siena scalp EEG dataset. The results obtained were compared with latest patient-specific seizure state detection methods. The proposed model achieved best patient-specific results despite the challenges of varying channels, recording duration, and seizure intervals.
  Keywords: Machine Learning; Deep Learning; Epilepsy; Seizures; EEG.
  DOI: 10.1504/IJBET.2025.10075724
   
  
• Automated Brain Tumour Identification through MRI Data Analysis based on Convolutional Neural Networks  
  by Amandeep Kaur, Kuldeep Singh, Prabhpreet Kaur 
  Abstract: Brain tumour classification is a significant research area in medical imaging. Manual examination of MRI scans is time-consuming, laborious, and may lead to imprecise findings. With the growth of artificial intelligence, automated methods are increasingly used for accurate detection of different brain tumour types. This paper presents a computer-aided diagnostic technique based on a 16-layer CNN architecture for precise tumour classification. The MR images are first resized and normalised, followed by dataset balancing using a hybrid SMOTE-edited nearest neighbour method. The balanced images are then fed into the proposed CNN model. A CNN-based feature extractor is also used with machine-learning classifiers including random forest, kNN, SVM, na
  Keywords: Brain Tumor; Deep Learning; Healthcare; Machine Learning; MRI.
  DOI: 10.1504/IJBET.2025.10076018
   
  
• Hemodynamic Analysis of Aortic BMHV under Different Leaflet Shapes  
  by Minzu Zhang, Yan Qiang, Tianci Duan, Liang Qi, Zhixiong Li 
  Abstract: Bileaflet Mechanical Heart Valves (BMHV) are clinically used to replace diseased heart valves. This study models the aortic root structure using medical imaging data and employs numerical simulations to analyze the hemodynamic characteristics of BMHV with different leaflet curvatures under pulsatile flow. Simulation results show that curved leafletsby increasing the middle jet orifice area improve the uniformity of three-jet flow distribution. Increasing leaflet curvature makes curved leaflets induce rotational fluid motion on their surface, causing more chaotic downstream vortex distributions but gradually reducing vortex intensity. With higher leaflet curvature, time-averaged wall shear stress (TAWSS) decreases, while oscillatory shear-index (OSI) tends to increase in the ascending aorta. Regions with low wall shear stress (WSS) and high OSI usually have higher relative residence time (RRT), and transverse oscillatory shear index (OSItr) also decreases with increasing leaflet curvature. Thus, optimising mechanical valve leaflet design to improve hemodynamic performance can reduce postoperative complications.
  Keywords: Bileaflet Mechanical Heart Valve; Computational Fluid Dynamics; Hemodynamics; Dynamic mesh.