International Journal of Biomedical Engineering and Technology (4 papers in press)

Regular Issues

• A Template-based One-Dimensional Gaussian Filter with Down-sampling for ECG Signal Denoising  
  by Tongnan Xia, Fu Yan, Bei Wang, Enruo Huang, Laiwu Zhang, Guoqiang Lou, Ming Liu, Yaojie Sun 
  Abstract: Noise reduction is a critical step in electrocardiogram (ECG) preprocessing, as it directly affects the accuracy of subsequent diagnoses and analyses. This study proposes a template-based one-dimensional Gaussian filter derived from a two-dimensional spatial-domain Gaussian kernel using a structured down-sampling strategy. Unlike conventional one-dimensional Gaussian filters, the proposed method leverages the probabilistic structure of the Gaussian distribution to enhance denoising performance while maintaining low computational complexity. Experiments on synthetic signals with multiple noise types, together with ECG recordings from the MIT-BIH Arrhythmia Database and the MIT-BIH noise stress test database, demonstrate consistent improvements in signal-to-noise ratio (SNR) across a range of noise conditions. Comparative evaluations further show that, under identical kernel configurations, the proposed method achieves higher SNR values than MATLABs built-in Gaussian filter while preserving waveform fidelity. Morphology-oriented metrics, including percent root-mean-square difference (PRD), correlation coefficients, and R-peak amplitude errors, together with paired-sample t-test analysis, confirm that the proposed filter effectively suppresses noise without introducing systematic amplitude bias or notable waveform distortion. These results indicate that the proposed approach offers a practical, lightweight, and reliable solution for real-time ECG denoising and other one-dimensional biomedical signal-processing applications, particularly in resource-constrained environments.
  Keywords: signal denoising; gaussian filter; electrocardiogram (ECG); signal-to-Noise ratio (SNR); noise reduction algorithms.
  DOI: 10.1504/IJBET.2026.10079884
   
  
• Systematic Review on Engineered Biomaterials for Cardiac Regeneration and Repair: From Emerging Technologies to Translational Barriers  
  by Jahanzeb Sheikh, Nabeeha Sahar, Kah Meng Leong, Jawad Shafique, Sidra Abid Syed, Tan Tian Swee, Syafiqah Saidin, Madeeha Sadia, Maheza Irna Mohamad Salim, Jose Javier Serrano, Umme Rubab 
  Abstract: Cardiovascular diseases (CVDs) remain the leading cause of death worldwide with limited regenerative capacity of adult cardiomyocytes posing a critical barrier to effective treatment. Traditional therapies such as pharmacological interventions, surgical revascularisation and organ transplantation provide symptomatic relief or functional stabilisation but fall short in reversing myocardial damage or promoting tissue regeneration. On the opposite end of the spectrum, engineered biomaterials represent a transformative paradigm in cardiac tissue engineering by providing structural support biochemical cues and electromechanical integration. These materials serve not only as scaffolds for cell delivery and retention but also as dynamic platforms to enhance angiogenesis, modulate inflammation and restore electrical conductivity. This review provides a comprehensive overview of current biomaterial strategies for cardiac regeneration including hydrogels, cardiac patches, stents and organoid platforms and classifies them by composition and functionality. Furthermore, it critically discusses the challenges in clinical translation such as immune responses, mechanical mismatch and delivery limitations. Finally, the review explores emerging technologies including 3D and 4D bioprinting, smart and stimuli responsive materials and extracellular vesicle-based the
  Keywords: Cardiovascular diseases; Cardiac tissue engineering; Biomaterials; Myocardial regeneration; Hydrogels; Cardiac patches; Stents; Angiogenesis; Clinical translation; Regenerative medicine.
  
  
• TriFormer-Seg: Leveraging Multi-Scale Vision Transformers and CNNs for Clinically Accurate Breast Cancer Segmentation  
  by Sri Vidhya Komarina, Ravi Kumar Jatoth 
  Abstract: This study introduces TriFormer-Seg, a hybrid deep learning model designed to perform accurate semantic segmentation of breast cancer in both MRI and histopathology images. The architecture leverages the distinct advantages of three complementary components: vision transformers for global contextual understanding, Swin transformers for multi-scale hierarchical representation, and convolutional neural networks (CNNs) for extracting fine-grained local details. By combining these methods, the model addresses the complex nature of medical imaging, where capturing both broad structural patterns and precise boundary details is essential. A streamlined decoding pathway, enhanced with boundary refinement, improves edge accuracy, while a specialised multi-scale fusion module harmonises information across different feature levels. Evaluated on datasets comprising dynamic contrast-enhanced MRI and histopathological images, TriFormer-Seg demonstrates consistent improvements over leading segmentation techniques, achieving Dice coefficients of up to 89.25% on histopathology images and 0.82 on breast DCE-MRI. Visual comparisons further confirm the models ability to generate segmentation masks closely aligned with expert annotations, indicating strong performance within the evaluated datasets under varying tissue appearance and imaging conditions.
  Keywords: Breast cancer; Semantic segmentation; Histopathology; Transformers; Deep learning; MRI Image.
  DOI: 10.1504/IJBET.2026.10080415
   
  
• A Segmentation-Free Approach for Multi-Class Classification of Pap Smear Images  
  by Nahida Nazir, Abid Sarwar 
  Abstract: Cervical cancer has become the primary cause of early death in women. To address this issue, this study explores a segmentation-free feature extraction approach utilizing pre-trained convolutional neural networks (CNNs), specifically VGG16, VGG19, and ResNet-50, for classifying clinical Pap smear images to aid in cervical cancer diagnosis. A dataset comprising 843 Pap smear images was utilized, and features were extracted from each model, which were subsequently fed into an XGBoost classifier. Class-wise metrics, including accuracy, precision, specificity, recall, and F1 score, were analyzed across seven cytological classes. VGG19 achieved the highest overall accuracy of 96.29%, followed by ResNet50 (95.43%) and VGG16 (94.57%), demonstrating its robustness in detecting both normal and abnormal cases. VGG19 also excelled in high-grade and moderate dysplasia classes, achieving an average F1 score of 0.93 across all categories. ResNet-50 performed competitively, while VGG16 showed limitations in certain critical classes.
  Keywords: Cervical Cancer; Deep Learning; Human Papillomavirus Smear; XGBoost; ResNet50.
  DOI: 10.1504/IJBET.2026.10080813