International Journal of Biomedical Engineering and Technology (9 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
   
  
• Development and implementation of an axial dynamic loading device for accelerated bone healing  
  by Sujan Krishna Samanta, Rajib Gupta, Nafisa Islam, Raima Mullick, Rajashree Dhua, Akshay Kumar Pramanick, Sourav Debnath 
  Abstract: This study aimed to develop a vibrating instrument to apply axial dynamic loading during wound healing with titanium-doped beta-tricalcium phosphate (Ti-β-TCP) implants. Healing outcomes were compared in rabbits using clinical, radiographic, histological, oxytetracycline labelling, micro-CT, and SEM analyses. Bone defects were created in the femoral condyle of three animal groups: unfilled controls (Group I), Ti-β-TCP implants without loading (Group II), and Ti-β-TCP implants with dynamic loading (Group III). Over two months, no acute inflammatory reactions were observed. Implant degradation indicated new bone formation, most pronounced in Group III. Histology showed improved bony structures with Haversian canals and satisfactory tissue regeneration in loaded implants. Oxytetracycline labelling confirmed higher new bone deposition in Group III. Micro-CT showed enhanced bone regeneration via implant degradation, and SEM revealed nearly bridged bone-implant interfaces in loaded samples. Overall, dynamic loading improved bone healing and implant integration compared to static conditions.
  Keywords: dynamic loading; bone regeneration; micro CT; oxytetracycline; histology.
  DOI: 10.1504/IJBET.2025.10073786
   
  
• Effects of different lunges on lower limb muscle activation and patellofemoral joint loading in individuals with patellofemoral pain syndrome  
  by Xiaowei Yang, Boshi Xue, Dong Sun, Kexin Yang, Zhipeng Zhou, Xuanzhen Cen 
  Abstract: Squats with lateral resistance are used in patellofemoral pain syndrome (PFPS) rehabilitation to target vastus medialis obliquus (VMO) activation, but evidence is inconsistent, and patellofemoral joint loading in these variations remains understudied. This study was to determine the differences in lower-limb muscle activation and patellofemoral joint loading during three lunge squats in PFPS individuals. Twenty-nine college athletes with PFPS performed three lunges: traditional lunge, hip-adduction lunge, and hip-abduction lunge. One-way repeated measures ANOVA was used to compare the variables of interest among the three lunges. The results demonstrated that hip-abduction lunge significantly increased the activation of the gluteus maximus and gluteus medius while concurrently reducing patellofemoral joint loading (descent phase: 2.39 ± 0.72 N/kg; ascent phase: 2.48 ± 0.61 N/kg). For PFPS individuals, the hip-adduction lunge may be more appropriate than the other two lunges when exercising the hip muscles and minimising patellofemoral joint loading.
  Keywords: lunge squat; hip adduction; knee biomechanics; patellofemoral joint force; neuromuscular control.
  DOI: 10.1504/IJBET.2025.10073737
   
  
• Retinal vascular segmentation using deep learning reinforced by discrete wavelet transform  
  by Mohamed Elssaleh Bachiri 
  Abstract: The detection of blood vessels in the retina helps to identify various diseases such as diabetes and hypertension. Detection of vessels is a complex task facing specialists during the segmentation process especially children's blood vessels, which are thin. We proposed a deep learning model to do the semantic segmentation of blood vessels in general and the identification of vessels with very high accuracy, where we used short discrete wavelet transform to enhance the features extracted from the deep learning that we created to fit the waves. We applied different types of discrete waves with varying scaling within the model to accurately detect vessels. In addition, we used these waves on other models of DL used for vascular segmentation, where the yield improved significantly after these additions. The experiments on the Digital Retinal Images for Vessel Extraction (DRIVE) database were our model achieved the best results with test F1-score and accuracy of 0.9873, 0.9787, respectively.
  Keywords: DWT2; deep learning; residual; retinal segmentation; U-net.
  DOI: 10.1504/IJBET.2025.10073959
   
  
• Hybrid deep learning approach for improving the diagnosis of lung cancer disease  
  by Shaik Bhasha, B.N. Jagadesh 
  Abstract: Lung cancer is recognised as the most severe disease that affects humans and frequently results in mortality when compared to other cancer conditions. Lung cancer cannot be detected early because it exhibits no symptoms. However, early identification of lung cancer contributes to people's continued survival rate. Computer technology has recently been employed to solve these diagnostic issues. In this research, we propose a hybrid deep-learning method for predicting lung cancer. An enhanced MobileNetV3 (EMobileNetV3) is proposed to predict the probability of lung cancer. The DenseNet-169 model is used to extract the features. An effective osprey optimisation algorithm (Os-OA) is also presented to adjust the proposed classification model's parameters to improve the classification performance. Compared to other existing models, the proposed model performed better, obtaining an accuracy of 98% and an AUC of 96% for dataset 1 and an accuracy of 99.30% and an AUC of 96.6% for dataset 2.
  Keywords: deep learning; mean imputation; min-max scaling; DenseNet-169; EMobileNetV3.
  DOI: 10.1504/IJBET.2026.10075704