https://jeeemi.org/index.php/jeeemi/issue/feed 2026-08-03T20:45:37+07:00 Dr. Triwiyanto editorial.jeeemi@gmail.com Open Journal Systems <p>The Journal of Electronics, Electromedical Engineering, and Medical Informatics, (JEEEMI), is a peer-reviewed periodical scientific journal aimed at publishing research results of the Journal focus areas. The Journal is published by the Department of Electromedical Engineering, Health Polytechnic of Surabaya, Ministry of Health, Indonesia. The role of the Journal is to facilitate contacts between research centers and the industry. The aspiration of the Editors is to publish high-quality scientific professional papers presenting works of significant scientific teams, experienced and well-established authors as well as postgraduate students and beginning researchers. All articles are subject to anonymous review processes by at least two independent expert reviewers prior to publishing on the International Journal of Electronics, Electromedical Engineering, and Medical Informatics website.</p> https://jeeemi.org/index.php/jeeemi/article/view/1724 2026-08-03T20:45:37+07:00 Noor S. eng.noor.salman@uobabylon.edu.iq Ehab Abdulrazzaq Hussein dr.ehab@uobabylon.edu.iq Laith Ali Abdul-Rahaim drlaithanzy@uobabylon.edu.iq

<p>Cardiovascular diseases require early and reliable screening because manual auscultation may be affected by noise, subjective interpretation, and inter-observer variability. This study aimed to develop an STFT-based deep learning framework for multiclass phonocardiogram (PCG) classification. The proposed framework was designed to provide a reproducible evaluation procedure by combining standardized preprocessing, time–frequency feature extraction, and deep learning-based classification under the same experimental conditions. Unlike approaches that may evaluate segmented signals without clearly preserving recording-level separation, this study emphasizes a leakage-free splitting strategy to reduce the risk of overestimated performance and to provide a more reliable assessment of model generalization. The Yaseen PCG dataset, consisting of 1000 recordings from five classes (AS, MR, MS, MVP, and Normal), was divided using a leakage-free recording-level split before segmentation and spectrogram generation. After preprocessing, 2-second PCG segments with 50% overlap were converted into 128 × 128 STFT spectrograms and classified using BiLSTM and CNN-BiLSTM models. Both models were trained and tested using the same dataset split, preprocessing pipeline, and evaluation metrics, including accuracy, precision, recall, F1-score, specificity, and confusion matrices. The BiLSTM model achieved 92.36% accuracy in the final independent test run, while the CNN-BiLSTM model achieved 95.83%. Across three repeated runs, BiLSTM achieved 93.85% ± 1.47%, whereas CNN-BiLSTM achieved 95.94% ± 0.71%. These results show that CNN-BiLSTM provides higher and more stable classification performance for five-class PCG classification, while BiLSTM remains a simpler alternative for lightweight implementation. Overall, the proposed STFT-based framework provides a reliable approach for automated heart sound classification and may support future computer-aided cardiac screening applications.</p>

2026-08-03T20:39:30+07:00 Copyright (c) 2026 Noor S., Ehab Abdulrazzaq Hussein, Laith Ali Abdul-Rahaim