International Journal of Biomedical Engineering and Technology (12 papers in press)

Regular Issues

• An Integrated Data-Driven Analysis-Based Deep Learning Framework for Early Autism Detection in Children to Improve Diagnostic Performance  
  by Jahanara Shaik, R. Shekhar, Chetan Shelke 
  Abstract: Autism spectrum disorder (ASD) children must be recognised early to obtain prompt care, promote development, and reduce long-term issues. This research provides a VGG16 and ResNet50-based data-driven deep learning system for early ASD screening using facial picture data. The study meticulously normalises, augments, and selects features using chi-square methods to ensure high-quality inputs and low dataset variability. Hyperparameter adjustment optimises model performance and five-fold cross-validation provides robust evaluation. VGG16 can recognise complex face characteristics with 87% accuracy for autistic classifications due to its precision and recall measures. Bio-inspired optimisation improves classification, helping ResNet50 outperform training epochs. Despite these advances, multimodal inputs are still needed for complete analysis due to the limits of facial data and the diversity of datasets. Deep learning models with feature selection can improve diagnostic precision, reduce false positives, and enable clinical real-time ASD screening. The proposed framework speeds diagnosis and is adaptable to varied healthcare circumstances. Future studies will focus on behavioural and genetic data, expandable artificial intelligence (XAI) for interpretability, and larger datasets for robustness. A scalable and effective ASD diagnosis using AI shows the transformative potential of AI in healthcare.
  Keywords: Autism Spectrum Disorder; Deep Learning; VGG16; ResNet50; Early Diagnosis; Explainable AI (XAI); Early Autism Detection.
  DOI: 10.1504/IJBET.2024.10068985
   
  
• Major Mandible Reconstruction: Design, Analysis, and Additive Manufacturing of Customised Implant and Surgical Osteotomy Guide  
  by Hari Narayan Singh, Yashwant Kumar Modi, A. Kuthe, Sanat Agrawal 
  Abstract: A patient-specific major mandible reconstruction has been presented in this article. During the process, a customized surgical osteotomy resection guide was designed to avoid overcutting and undercutting of the infected part of diseased mandible. The reconstructed bone models and customized mandible implant were 3D printed to test form and fit of the implant. Finite element analysis was used to simulate the distribution of von Mises stress and deformation in Ti6Al4V implant by subjecting it to an occlusal bite force of 300 N, replicating the forces experienced during biting. The analysis was performed for four different biting scenarios. The highest values for maximum von Mises stress and maximum deformation was observed when the biting force was applied at the incisor. Despite variations in stress and deformation, the Ti6Al4V implant was determined to be safe in all four biting scenarios.
  Keywords: Mandible reconstruction; FEM analysis; 3D printing; customized mandible implant; and additive manufacturing.
  DOI: 10.1504/IJBET.2024.10068993
   
  
• Fusion of Multiple Time-Frequency Representation Techniques and Classifiers for ECG and PPG Signal Analysis  
  by Piyush Mahajan, Amit Kaul 
  Abstract: This study explores the fusion of multiple Time-Frequency Representation (TFR) techniques for analyzing Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals. We examined 11 TFR methods for noise removal, characteristic point detection, and feature extraction. Multiple classifiers were trained to classify ECG signals into six arrhythmia classes and PPG signals for hypertension detection. The ensemble classifiers, particularly those combining Continuous Wavelet Transform (CWT), S-Transform, Wigner-Ville Distribution (WVD), and Synchrosqueezed Wavelet Transform (SSWT), achieved a testing accuracy of 96.6% for ECG signals. A combination of CWT and SSWT with the K-Nearest Neighbour (KNN) classifier achieved 81.48% accuracy on the PPG dataset. The ensemble approach using majority voting significantly enhanced classification performance, reaching 99.75% accuracy for ECG arrhythmia and 82.52% for PPG classification. This fusion of TFR techniques and ensemble classifiers demonstrates improved accuracy in signal classification tasks.
  Keywords: ECG;PPG;TFR;ML.
  DOI: 10.1504/IJBET.2024.10069147
   
  
• Hybrid Approach for Skin Lesion Analysis:- Integrating Modified U-Net Segmentation with Vision Transformers for Multi-Class Skin Cancer Detection  
  by Ramya J, Anil Kumar K.M 
  Abstract: Skin cancer is a major global health concern, where early and accurate detection is crucial for patient survival. Traditional CNN-based methods in skin lesion classification face challenges, particularly with the complexity of spatial and semantic features. To address these issues, we propose a unique hybrid deep learning approach integrating vision transformer (ViT) and a modified U-Net model. ViT processes images as tokens instead of pixels, enabling superior feature extraction and classification. Pre-processing techniques, including non-local means filtering for denoising and unsharp masking for contrast enhancement, are applied to enhance model robustness. Our hybrid approach integrates U-Net for precise segmentation, achieving metrics such as IOU of 92.46%, AUC of 97.64%, and dice coefficient of 95.96%. ViT for classification achieves exceptional accuracy, precision, recall, and F1 score, all at 99%. Using the HAM10000 dataset, our method surpasses existing techniques, demonstrating remarkable effectiveness in skin cancer detection and classification.
  Keywords: Skin Cancer Images; Multi-class classification; Vision Transformers; Region-of-Interest Segmentation; Computerized; Medical image processing.
  DOI: 10.1504/IJBET.2025.10069272
   
  
• Alzheimer's Disease Recognition Based on Multimodal Image Fusion  
  by Xinjie Tao, Lisheng Wei, ShengBo Zhu 
  Abstract: To improve diagnostic accuracy for Alzheimer's Disease (AD) and enhance lesion feature extraction from single-modality imaging, a deep learning-based multimodal image fusion classification method is proposed. A novel residual network extracts features from three-dimensional images, serving as a feature extractor for both Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). The extracted features are fused for classification, enhanced by a coordinate attention mechanism to capture spatial and channel relationships in 3D medical images. Experimental results show the fusion network achieved 91.07% accuracy in AD/Mild Cognitive Impairment (MCI)/Cognitively Normal (CN) classification, improving by 7.14% over the basic residual network, 21.43% over single-modality MRI, and 12.5% over single-modality PET. The fusion network also shows performance improvements in AD/CN, AD/MCI, and CN/MCI classification tasks, demonstrating its effectiveness.
  Keywords: Alzheimer's Disease; Multimodal feature fusion; Attention mechanism; Residual network.
  DOI: 10.1504/IJBET.2025.10069613
   
  
• Eye-blink Artifact Removal Framework for EEG Signals using DWT and Autoencoder  
  by Mohd. Faisal, Sudarsan Sahoo, Jupitara Hazarika 
  Abstract: This paper presents an automatic method for removing eye-blink artifacts from contaminated EEG signals, achieving high Signal to Artifact Ratio (SAR) and correlation coefficient (CC) with minimal computational time. In the proposed method, the autoencoder is trained to learn the underlying structure of artifacts within the contaminated frequency band of the EEG signal. Once trained, the autoencoder can effectively remove artifacts from contaminated signal by processing the contaminated signal as its input. Loss function plays important role in reconstructing the input signal. A novel loss function comprising correlation coefficient and mean square error is used to minimize the reconstruction error. The performance of the proposed method is measured by SAR and CC, achieving average values of 3.21 and 0.88, respectively, in removing artifacts from EEG signals. Also, the proposed method achieved minimum computational time in comparison to other methods which is 9 ms.
  Keywords: Brain computer Interface; electroencephalogram; eye-blink artifact; autoencoder.
  DOI: 10.1504/IJBET.2025.10070025
   
  
• Improving Pulmonary Disease Detection through Autoregressive Features and K-Nearest Neighbours Classifier  
  by Alireza Golkarieh, Amirhosein Dolatabadi, Parsa Saei, Omid Rezaei, Fatemeh Dehghani 
  Abstract: The classification of lung sound signals, concerning autoregressive modeling and machine learning algorithms, is the main objective of this study. Lung sounds recorded from 112 subjects with three filter modes (Bell, Diaphragm, Extended) were then classified into two groups: healthy and pulmonary conditions like asthma, COPD, and pneumonia. The AR modelling extracted five essential features for all modes, combined with a classifier using K-Nearest Neighbors (KNN). During training, it gives an accuracy of 98.3% for the unhealthy and 95.5% for the healthy cases, whereas during testing, the results were 100% and 92.3%, respectively. The overall accuracy was 98.2%. Dimensionality was reduced with decreased computational load using the method of AR; therefore, the simple model KNN achieved high accuracy. This efficiency makes the approach suitable for hardware implementation in portable, point-of-care diagnostic devices and thus helps in respiratory disease diagnosis in remote and clinical settings.
  Keywords: Lung sounds; Autoregressive modeling; K-Nearest Neighbors; Pulmonary disease classification; Machine learning.
  DOI: 10.1504/IJBET.2025.10070540
   
  
• Variations in Cardiac Associated Sympathovagal Oscillations under Short-Term Heat Exposure  
  by Kumari Akanksha, Rahul Kumar, Nitya Garg, Yogender Aggarwal, Rakesh Kumar Sinha 
  Abstract: Heat waves alter the autonomic nervous system (ANS) measured through heart rate variability (HRV) and pulse rate variability (PRV). Therefore, the present study aimed to examine the ANS oscillations to significant heat stress stimuli. Digital Lead-II electrocardiogram (ECG) and pulse plethysmogram (PPG) were recorded from control and heat-stressed groups under anesthetized conditions. Tachograms were generated from both ECG and PPG signals. The fast Fourier transform (FFT) was used to analyse the bands using Kubios 3.5.0 software. An unpaired t-test and Bland-Altman test were employed to compare the differences between the two groups. The results demonstrated a significant shift of oscillations toward sympathetic dominance with the withdrawal of parasympathetic activity under heat exposure. The LF oscillations increased and exhibited strong associations between HRV and PRV parameters under the heat stress group. PRV and HRV assess the ANS oscillations and aided with valuable insights into cardiac autonomic function.
  Keywords: Autonomic nervous system; fast Fourier transform; Heat exposure; Heart rate variability; Pulse rate variability; Sympathovagal balance.
  DOI: 10.1504/IJBET.2025.10070752
   
  
• 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 normalization are employed to overcome dataset limitations and improve model generalization. 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; PLI; PLV.
  DOI: 10.1504/IJBET.2025.10072009
   
  
• The Digital Twin Revolution in Personalised Medicine  
  by Rama Rao Tadikonda, Vasavi Sai Saraswati Rayapudi, Arthika Chauhan Laudia, Saniya Mehrin 
  Abstract: The digital transformation of the health service will be driven by technology under the expanding concept of precision healthcare Future precision medicine is anticipated to embrace personalized diagnostic and treatment plans for each patient, as simulation plays significant part in medicine The advancement of digital twin (DT) technology will render this sort of personalisation possible A digital twin is a virtual representation of a physical entity that has dynamic, mutual connections with its digital counterpart These digital twins have an immense capacity to completely revolutionise healthcare by minimising expenses, raising standards in medical education, research, enhancing patient results and care. The review of this literature discusses the applications of digital twins in medical sector, suggested frameworks, the significance of digital twin for attaining precision healthcare, cyber-security challenges, and ethical consequences for this novel approach which are covered in a greater depth.
  Keywords: Digital Twin; Artificial Intelligence; Cyber technology; Precision healthcare.
  DOI: 10.1504/IJBET.2025.10072020
   
  
• 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 summarize 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 was 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: fetal; finite element; model; childbirth; labor; biomechanics; interactions; clinical; application.
  DOI: 10.1504/IJBET.2025.10072083