International Journal of Biomedical Engineering and Technology (16 papers in press)

Regular Issues

• Non-Contact Pulse Rate Estimation from Remote Photoplethysmography using Wavelet Transform Filter and Convolutional Neural Network  
  by Hoang Thi Yen, Doan Van Sang, Van-Phuc Hoang, Guanghao Sun 
  Abstract: Pulse rate (PR) measurement traditionally relies on contact sensors such as photoplethysmography (PPG). However, these are unsuitable during pandemics and for long-term monitoring. Remote photoplethysmography (rPPG) offers contactless cardiovascular monitoring by detecting blood pulsation-induced skin colour changes. While conventional rPPG methods suffer from poor accuracy due to motion and lighting sensitivity, deep learning approaches, though more accurate, require extensive datasets and computational resources while lacking interpretability. This study presents an improved hybrid approach combining conventional preprocessing with deep learning. Video data undergoes traditional processing, band-pass filtering to enhance PR frequencies, and continuous wavelet transform to generate time-frequency images. These feed into a streamlined convolutional neural network (CNN) which is designed to iteratively extract features, resulting in a network that is not overly complex for hardware deployment. Results demonstrate superior performance with 1.9 bpm RMSE, surpassing previous studies through the synergy of traditional preprocessing and CNN-based estimation. This research advances non-contact RGB camera applications in vital sign monitoring.
  Keywords: camera-based; pulse rate; remote photoplethysmography; wavelet filter; convolutional neural network.
  DOI: 10.1504/IJBET.2025.10073137
   
  
• Brain Age Estimation using Cross Entropy Loss and Sparse Autoencoder Features: NeuroAgeNet, a Deep Graph Neural Network  
  by Soumya Kumari L. K, R. Sundarrajan 
  Abstract: MRI brain age evaluations may detect age-related neurological disorders early. Convolutional structures provide global structural information but overlook local neurological changes in standard DL models, restricting their applicability in different populations and high-dimensional neuroimaging. In NeuroAgeNet, DGNN and functional/anatomical brain interregional correlations predict brain age. SAs efficiently reduce high-dimensional input to compact feature representations, improving generality and eliminating redundancy. The regression-based aim solves class imbalance in discrete age groups and evaluates age-related patterns using a modified cross-entropy loss function. Multi-head self-attention and SAG Pool help DGNN layers filter node-level brain connection graphs and capture hierarchical dependencies. Experimental T1- and T2-weighted multi-site structural MRI using IXI dataset. NeuroAgeNet obtained 1.903
  Keywords: Brain Age Estimation; Deep learning; Magnetic Resonance Imaging; Deep Graph Neural Networks; Cross-entropy Loss Function; Sparse Autoencoder; Neuroimaging.
  DOI: 10.1504/IJBET.2025.10073234
   
  
• An Archimedean Spiral Antenna Integrated with an Epsilon Negative Metamaterial Reflector as Microwave Hyperthermia Applicator for Non-Invasive Treatment of Early-Stage Skin Cancer  
  by Komalpreet Kaur, Amanpreet Kaur 
  Abstract: Microwave hyperthermia (MHT) is an emerging non-invasive technique for treating cancer by selectively raising tumour temperatures to therapeutic levels. This manuscript presents the analysis and validation of a MHT applicator for treating both superficial and deep malignancies in the skin. This article shows the analysis and validation of a microwave hyperthermia (MHT) applicator for treating both superficial and deep malignancies in the skin. The proposed applicator consists of an Archimedean spiral micro-strip patch antenna (ASMPA) backed by epsilon-negative (ENG) metamaterial reflector (48
  Keywords: Skin cancer; Archimedean Spiral Antenna; ENG metamaterial; Microwave Hyperthermia; temperature distribution.
  DOI: 10.1504/IJBET.2025.10073238
   
  
• Radiographic Analysis of Foot Bone Alignment Changes across Different High Heel Heights: a Case Study  
  by Hongbin Chang, Meizi Wang, Yang Song, Xuanzhen Cen, Qiaolin Zhang 
  Abstract: While the biomechanical effects of high heels (HHs) have been widely studied, their impact on foot morphology remains unclear. This study assessed variations in foot alignment across heel heights using radiographic analysis. Three healthy female subjects underwent weight-bearing X-rays of the right foot at five heel heights (0, 3, 5, 7, 9 cm) from lateral, anterior-posterior, and hindfoot perspectives. Twenty morphological parameters were measured independently by two investigators. Measurements showed high interobserver reliability. Deviations became evident at 5 cm and increased up to 9 cm. The midfoot demonstrated the greatest change, with arch height rising substantially. The forefoot revealed hallux valgus tendencies, while the hindfoot showed smaller changes but reduced ankle stability. Increasing heel height alters foot bone architecture, promoting deformities that may contribute to foot pathologies with prolonged high-heel use.
  Keywords: Foot morphology; Bone alignment; Radiographic angles; X-ray; High heels.
  DOI: 10.1504/IJBET.2025.10073275
   
  
• Lower Limb Biomechanical Characteristics of People with Different BMIs during Rope Skipping Exercise  
  by Jihan Chen, Zanni Zhang, Zhifeng Zhou, Xiuye Qu, Huiyu Zhou, Datao Xu 
  Abstract: This study analysed the biomechanical characteristics of the lower extremities during rope skipping in individuals with varying BMI levels. Thirty participants (normal weight, overweight, and obese, n=10 each) performed standardised jump rope trials while 3D motion capture (Vicon), ground reaction forces (AMTI), and electromyography (EMG, Delsys) data were collected. OpenSim modelling with inverse kinematics and dynamics was used to analyse joint biomechanics, and EMG signals were processed (6 Hz filter, MVC-normalized) and compared to model predictions for validation. The findings revealed that obese individuals exhibited reduced ankle dorsiflexion angles, higher valgus moments, and earlier peak muscle forces in the lower legs during rope skipping, leading to shorter landing cushioning times and increased pressure on the ankle and knee joints. These factors may contribute to a higher risk of lower limb injuries, including patellofemoral pain syndrome, anterior cruciate ligament injuries, Achilles tendinitis, and plantar fasciitis.
  Keywords: Body Mass Index; Rope Skipping; Lower Limb Biomechanics; Kinetics; Kinematics; Joint Force.
  DOI: 10.1504/IJBET.2025.10073598
   
  
• 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 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
  Keywords: Lunge squat; Hip adduction; Knee biomechanics; Patellofemoral joint force; Neuromuscular control.
  DOI: 10.1504/IJBET.2025.10073737
   
  
• Development and Implementation of an Axial Dynamic Loading Device for Accelerated Bone Healing  
  by SujanKrishna Samanta, Rajib Gupta, Nafisa Islam, Raima Mullick, Rajashree Dhua, Akshay 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--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--TCP implants without loading (Group II), and Ti--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: TCP; Dynamic load; SEM; Micro CT.
  DOI: 10.1504/IJBET.2025.10073786
   
  
• 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
   
  
• Retinal Vascular Segmentation using Deep Learning Reinforced by Discrete Wavelet Transform  
  by Bachiri Mohamed Elssaleh  
  Abstract: 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 DRIVE database (Digital Retinal Images for Vessel Extraction) 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
   
  
• Epileptic Seizure Prediction using EEG Signals- a Survey  
  by Aarti Sharma 
  Abstract: Epilepsy is incurable disease of human brain that stimulates repeating seizures. A group of neurons of the human brain start firing synchronously as seizure approaches. The sudden and apparently unpredictable nature of epilepsy is one of the most disabling aspects. Recent research has explored that it is feasible to forecast the seizure in advance. Ample amount of research has been made in seizure prediction but still the representation of the current approach to the clinical application is not possible. It has been proven that seizure start with small flash of activity and takes hours to build. Therefore, automatic prediction of such activity has the potential to give precautionary warning to the patients so that harmful activities can be avoided. The present study reviews the most significant and recent methods for seizure prediction. The main difficulties in epilepsy prediction algorithms that have been identified during the preparation of this review study are feature selection and classification. The strategies presented in this study vary based on the features and classifiers used throughout the past few years. The approaches discussed will provide a thorough overview, suggestions, and directions for further research.
  Keywords: Epilepsy; Seizure; Alzheimer.
  DOI: 10.1504/IJBET.2025.10074196
   
  
• Explainable Ensemble Machine Learning Model for Autism Identification Using EEG and Optimised Feature Selection  
  by Anamika Ranaut, Padmavati Khandnor, Trilok Chand 
  Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that remains challenging to diagnose due to its reliance on subjective clinical assessments. To address this limitation, an automated Electroencephalography (EEG) based ASD identification framework is proposed, aiming to enhance identification accuracy. Temporal dynamics of EEG signals analyzed through three-phase feature selection approach involving independent samples t-test elimination, followed by a novel binary search-driven Mutual Information (BiS-MI) and Recursive Feature Elimination (BiS-RFE). Six ensemble models trained on the selected features, and feature importance and classification predictions interpreted using Explainable Artificial Intelligence (XAI). CatBoost achieved the highest performance, with an accuracy of 0.9923 and recall of 0.9864 using BiS-MI, and an accuracy of 0.9936 and recall of 0.9911 using BiS-RFE. SHAP analysis identified features from frontal and central EEG electrodes as the most significant contributors. These results highlight the potential for developing interpretable, non-invasive and improved diagnostic tools for ASD identification.
  Keywords: Autism; Electroencephalography; Ensemble models; Feature selection; Feature extraction; Mutual information; Recursive feature elimination; Statistical test.
  DOI: 10.1504/IJBET.2025.10074197
   
  
• Effects of Compression Garment Application on Running Performance: a Systematic Review  
  by Xeiyi Xu 
  Abstract: Despite the growing use of compression garments (CGs) in running, their effects on performance remain uncertain due to varied applications, diverse methodologies, and different types of CGs. This systematic review aims to synthesise existing literature, evaluating CGs impact on running performance through biomechanical, physiological, perceptual, and fatigue-related outcomes. According to the PRISMA-P guideline for systematic reviews, a comprehensive search was conducted on the four online databases for the article published up to the December 2024, involving Google Scholar, Web of Science, PubMed, and SPORTD. The systematic review included 15 articles, with 11 focusing on professional runners and four on recreational runners, comprising 241 male and 45 female participants, of whom 157 males and 36 females were well-trained runners. This systematic review of 15 studies assessed the effects of CGs on running-related outcomes, including physiological responses, biomechanical factors, performance metrics, perceived sensations, and muscle fatigue. Physiological responses, examined in ten studies, showed inconsistent evidence of CGs benefits in long-distance or short-term running. Biomechanical factors and performance remained largely unchanged across CGs types, pressures, and runner experience levels. While CGs reduced delayed onset muscle soreness, these garments had limited impact on running fatigue.
  Keywords: compression garment; running economy; muscle soreness; physiological response; running biomechanics.
  DOI: 10.1504/IJBET.2025.10074198
   
  
• Multilingual voice disorder classification using glottal flow and MFCC-based acoustic analysis  
  by Nitin Pal, Girish Gidaye, Varsha Turkar, Uma Jaishankar 
  Abstract: Vocal pathologies affect vocal fold dynamics, altering pitch, loudness, and voice quality. Conventional methods rely on invasive techniques. Many researchers have used machine learning models based on features extracted from speech signals. It may not fully capture physiological alterations in vocal fold impairments. To address these challenges, the work in this paper evaluates glottal flow features mined from true voice sources by comparing them against Mel-frequency cepstral coefficients (MFCC) based features across four linguistically diverse datasets. The proposed non-invasive method captures most physiological alterations in vocal fold impairments as the features are derived from true voice sources. The data augmentation, oversampling techniques and min-max normalisation are employed to overcome dataset limitations and improve model generalisation. Sustained vowel /a/ samples are used to train multiple classifiers for each dataset for comparative analysis. It is observed that classifiers using glottal flow features achieved superior performance compared to MFCC.
  Keywords: vocal pathology; classification; glottal flow features; pathological speech analysis; LSTM model; voice disorder detection; healthcare AI applications.
  DOI: 10.1504/IJBET.2025.10071999
   
  
• Analysing EEG-based differential functional connectivity patterns during truth and lie responses  
  by Sakshi Jethva, Jyoti Maheshwari 
  Abstract: With the growing application of machine learning and deep neural networks in biomedical signal processing, selecting the right features for model training has become crucial – especially in lie detection, where accurate classification has serious implications in forensics, law, national security, and research. This study emphasises the importance of feature selection by comparing functional connectivity (FC) networks during truth and lie conditions. Using a publicly available dataset, we applied multiple connectivity measures – correlation, phase locking value (PLV), phase lag index (PLI), coherence, and imaginary coherence (iCOH) – across frequency bands (global, delta, theta, alpha, beta, gamma). Results showed significantly higher FC during lying in global, delta, and theta bands, particularly in frontal-temporal regions, suggesting their relevance for deception detection. In contrast, alpha, beta, and gamma bands showed inconsistent FC patterns. These findings highlight the complex neural dynamics of lying and support the use of diverse connectivity measures to enhance the accuracy of lie detection systems.
  Keywords: coherence; connectivity; correlation; deception; phase lag index; PLI; phase locking value; PLV.
  DOI: 10.1504/IJBET.2025.10072009
   
  
• Advances and future directions of foetal finite element modelling in childbirth: from biomechanical interactions to clinical implications  
  by Linxiao Shen, Zhenghui Lu, Xin Li, Dong Sun, Yang Song, Gusztáv Fekete, András Kovács, Fan Li, Xuanzhen Cen 
  Abstract: This review aims to summarise the applications of finite element modelling used in labour mechanics and explore their clinical relevance in optimising labour management and intervention strategies. A systematic literature search was performed in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines across the PubMed, IEEE Xplore, Web of Science and Elsevier ScienceDirect databases. Selected 13 studies were evaluated based on the methodological quality assessment of single-subject finite element analysis (MQSSFE). The review highlights the widespread use of whole-body foetal finite element models in childbirth simulations. Key factors in childbirth biomechanics include uterine contraction intensity, abdominal muscle forces, pelvic floor function, foetal head flexion, tissue properties, and descent trajectory. Finite element modelling offers key insights but faces challenges in accuracy, personalised anatomy, and clinical application. Advances in computational biomechanics, imaging validation, and patient-specific simulations will improve childbirth understanding, risk assessment and labour management.
  Keywords: foetal; finite element model; childbirth; labour; biomechanics; interactions; clinical; application.
  DOI: 10.1504/IJBET.2025.10072083
   
  
• Improving dental X-ray image resolution with deep learning-based super-resolution techniques  
  by Vaishali Patel, Anand Mankodia 
  Abstract: Although the dental X-rays are useful for detecting and treating oral health problems, the low-resolution images they produce often make it hard for dental professionals to see fine details. This limitation occasionally leads to diagnostic challenges and even results in missed problems. As a means of addressing this issue, our study explored the application of deep learning approaches to sharpen and improve the quality of dental X-ray pictures, making them clearer and easier to interpret. We applied several deep learning methods, known for their success in enhancing image quality, to a dataset of dental X-rays. The results show significant improvements in clarity, with higher image quality scores – measured by metrics like PSNR and SSIM – that indicate a more detailed view of dental structures. These improvements could help dental professionals to catch issues earlier and make more accurate diagnoses. Our research demonstrates the potential of deep learning to change dental X-ray imaging, supporting better outcomes for patients and providing a useful tool for dental care providers.
  Keywords: dental X-ray imaging; super-resolution; machine learning; deep learning; convolution neural network; medical imaging; image processing.
  DOI: 10.1504/IJBET.2025.10073088