{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T08:23:41Z","timestamp":1777710221210,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"36","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Adaptive learning and automatic classification of ECG signals is one of the commonly processed and practical methods in cardiac anomalies detection with that area has a huge potential to teach clinicians for better clinical healthcare decision making and also remote health patient monitoring [7,8]. Past methods tackle issues like spatial\u2013temporal feature extraction, class imbalance and dataset generalisation, but they are limited by a number of shortcomings: the traditional ML models depend on hand-crafted features and thus lack scalability, and the stand-alone deep models (CNNs or LSTMs) do not capitalize on spatial and sequential information simultaneously. To overcome these shortages, we present HybridCardioNet, a joint deep-learning framework that integrates CNN-based spatial feature extraction and LSTM based temporal-dependency modelling. The ECG was filtered using band-pass filtering (0.5\u201340 Hz) to remove the low-frequency baseline-wander, z-score normalisation, and segmented into single-beat segments using the R-peak detection from the MIT-BIH Arrhythmia Database; class imbalance was handled via class-weighted loss (random minority oversampling provided validation) HybridCardioNet with stratified cross-validation gives 98.39% accuracy with the same balanced precision, recall and macro-F1 score. Against popular protocols in recent literature on MIT-BIH, it also achieves competitive performance against internal baselines (CNN-only, LSTM-only and classical ML). Thus, hybridCardioNet solves one key limitation of the previously existing methods. With regards to the other two limitations, since hybridCardioNet is able to outperform the state-of-the-art for multi-class ECG classification, hybridCardioNet will be appropriate for real-time applications in terms of early detection &amp; continuous ECG signals clinical\/remote monitoring.<\/jats:p>","DOI":"10.31449\/inf.v49i36.8140","type":"journal-article","created":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T21:00:28Z","timestamp":1767560428000},"source":"Crossref","is-referenced-by-count":1,"title":["HybridCardioNet: A CNN-LSTM-Based Deep Learning Framework for ECG Signal Classification and Cardiac Anomaly Detection"],"prefix":"10.31449","volume":"49","author":[{"given":"A","family":"Bharath","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G","family":"Merlin Sheeba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2025,12,20]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8140\/6034","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8140\/6082","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8140\/6034","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T21:00:29Z","timestamp":1767560429000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/8140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,20]]},"references-count":0,"journal-issue":{"issue":"36","published-online":{"date-parts":[[2026,1,4]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v49i36.8140","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12,20]]}}}