International Journal of Biomedical Engineering and Technology (5 papers in press)

Regular Issues

• 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
   
  
• Automated Brain Tumour Identification through MRI Data Analysis based on Convolutional Neural Networks  
  by Amandeep Kaur, Kuldeep Singh, Prabhpreet Kaur 
  Abstract: Brain tumour classification is a significant research area in medical imaging. Manual examination of MRI scans is time-consuming, laborious, and may lead to imprecise findings. With the growth of artificial intelligence, automated methods are increasingly used for accurate detection of different brain tumour types. This paper presents a computer-aided diagnostic technique based on a 16-layer CNN architecture for precise tumour classification. The MR images are first resized and normalised, followed by dataset balancing using a hybrid SMOTE-edited nearest neighbour method. The balanced images are then fed into the proposed CNN model. A CNN-based feature extractor is also used with machine-learning classifiers including random forest, kNN, SVM, na
  Keywords: Brain Tumor; Deep Learning; Healthcare; Machine Learning; MRI.
  DOI: 10.1504/IJBET.2025.10076018
   
  
• Reconstruction of Central Arterial Pressure Waveform based on CBL-iTransformer Model from Radial Arterial Pressure Waveform  
  by Xiaolu Li, Tao Peng, Libin Zhang, Hanguang Xiao 
  Abstract: Central arterial pressure (CAP) is a key parameter for assessing cardiovascular health and related disease risks. Accurate, non-invasive, and continuous reconstruction of CAP is crucial for cardiovascular disease evaluation, but traditional and some deep learning methods show limited precision and feature extraction ability. This study proposes the CBL-iTransformer model, which is aimed at improving the accuracy of CAP waveform reconstruction. The model is validated using radial arterial pressure and CAP data from patients, and its performance is compared with traditional and deep learning methods. The results demonstrate that the CBL-iTransformer model effectively reconstructs CAP waveforms and achieves reliable estimation for central aortic systolic pressure and diastolic pressure outperforms the compared models under the tested conditions. In addition, Bland-Altman analysis indicates a high level of agreement between the reconstructed and reference measurements.
  Keywords: Keywords: Cardiovascular System; Central Arterial Pressure; Deep Learning; iTransformer Model; BiLSTM; LSTM; Waveform Reconstruction.
  DOI: 10.1504/IJBET.2025.10076520
   
  
• 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