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

Regular Issues

• Real-Time Emotion Detection From Integrating Electroencephalography, Facial Expressions, and Speech : Review  
  by Aaditi More, Joydeep Sengupta 
  Abstract: Emotion recognition systems have gained substantial focus due to their pivotal role in man-machine interaction and affective computing applications. This comprehensive literature survey explores the latest advancements in the field, spanning a different variety of strategies and datasets. Survey delves into realm of cross-corpus speech emotion identification, discussing innovative approaches including deep local domain adaptation and multimodal systems like RobinNet. Furthermore, it investigates electroencephalography-based emotion recognition techniques, highlighting hierarchical self-attention networks, deep forest models, and spatio-temporal convolution attention neural networks. The paper also presents collaborative frameworks for the diagnosis of sadness that makes use of cross-scale facial feature analysis and negative emotion detection. In realm of machine learning, ensemble approaches for affective computing and the efficacy of prompt consistency in multi-label textual emotion detection are examined. Through this survey, emerging trends, comparative studies, and validation frameworks in emotion recognition systems research are synthesised. The findings underscore the significance of these systems in knowing human emotions and creating the groundwork for next developments in affective computing.
  Keywords: Speech Emotion Recognition; Electroencephalography Based Emotion Recognition; Deep Learning Models; Multimodal Emotion Recognition.
  DOI: 10.1504/IJBET.2025.10076819
   
  
• MA-TIL-GBNN: A Multi-Attention Enhanced Deep Incremental Learning Based Diabetes Prediction and Drug Recommendation Framework  
  by Netra S. Patil, Naveenkumar Jayakumar, Rohini B. Jadhav, Gauri R. Rao, Shashank D. Joshi, Shubhangi R. Katkar, Madhavi Mane 
  Abstract: This research is designed to develop an advanced framework for the prediction of diabetes and the personalized recommendation of drugs. This research proposes a Multi-Attention enhanced Task Incremental learning coupled Gradient Boost Deep Neural Network (MA-TIL-GBNN) approach aimed at the prediction of diabetes and recommendation of drugs based on their types.The approach integrates MA techniques into the DNN and Generative Adversarial Network-based data augmentation (GDA) to effectively analyse the health indicators. Additionally, the TIL enhances the training of data and the Light GBM facilitates efficient processing. The experimental results using Diabetes Health Indicators Dataset showed 97.79% accuracy, 98.22% sensitivity, and 97.36% specificity respectively.
  Keywords: Diabetes prediction; Drug Recommendation; Deep Learning; Machine Learning; Healthcare Management.
  DOI: 10.1504/IJBET.2026.10077448
   
  
• Simulation and design of an elliptical surface coil for small animal MRI at 3T  
  by Giulio Giovannetti, Benjamin Michael Hardy, Francesca Frijia, Alessandra Flori, Vincenzo Positano 
  Abstract: Custom-designed radiofrequency coils are commonly utilised in preclinical magnetic resonance imaging (MRI) to image small animals due to their cost-effectiveness and flexibility in adaptation to specific anatomical regions. Researchers frequently prefer such specialised coils for targeted metric assessment because their handcrafted nature allows for precise customisation. Rather than repeated experimental iterations, simulation-based refinement of coil architecture streamlines the design process. Numerical simulation methods offer more accurate estimations of signal-to-noise ratio (SNR) compared to magnetostatic models. The present study presents a comprehensive validation, using finite-difference time-domain (FDTD) full-wave simulation, of an elliptical radiofrequency (RF) coil tailored for small animal MRI applications. The approach incorporates calculations of coil and sample-induced resistances, inductance parameters, and magnetic field distributions under loading conditions with both phantom and whole-body mouse models. The accuracy of the simulation data is verified with data acquired from a transmit/receive elliptical coil prototype for a 3T MRI clinical scanner.
  Keywords: magnetic resonance; radiofrequency coils; inductance; magnetic field; resistance; signal-to-noise ratio; SNR.
  DOI: 10.1504/IJBET.2026.10078039
   
  
• Piezoelectricity in biomedical innovation: a systematic review of human-centric devices, applications, challenges and future directions  
  by Fatima Hassan, Taha Sana, Hamna Rana 
  Abstract: Piezoelectricity has emerged as a key mechanism in biomedical engineering, enabling localised electrical stimulation, sensing, and energy harvesting in human-centric devices. This systematic review analyses recent advances (2021-2025) in piezoelectric materials, device architectures, and biomedical applications, including tissue engineering, implantable and wearable systems, biosensors, neural interfaces, and drug delivery. Polymer-based materials such as PVDF exhibit superior flexibility and biocompatibility, whereas ceramic materials provide higher electromechanical efficiency but face limitations related to toxicity and mechanical mismatch. Despite their potential for self-powered operation and bioelectric modulation, clinical translation remains constrained by low power output, signal-to-noise limitations, material instability, and integration challenges with biological tissues. This review identifies material innovation, device miniaturisation, and system integration as key barriers to deployment, and highlights future directions toward lead-free nanomaterials, flexible hybrid electronics, and scalable biomedical applications.
  Keywords: piezoelectric biomaterials; implantable biomedical devices; bioelectric stimulation; self-powered sensing; flexible piezoelectric systems; clinical translation.