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However, automated murmur classification remains\n                challenging due to signal variability, class imbalance, and temporal dependence\n                within heart-sound sequences. This study presents a leakage-safe heart-sound\n                classification framework that combines peak-based segmentation, Mel-Frequency\n                Cepstral Coefficient (MFCC) feature extraction, Synthetic Minority Over-sampling\n                Technique (SMOTE)\u2013based class balancing, and Recurrent Neural Network (RNN)\u2013driven\n                temporal modeling. Segmentation was performed around cardiac onset peaks, and\n                evaluation was conducted using recording-level splits for the PhysioNet 2016 dataset\n                and patient-level splits for the PhysioNet 2022 dataset to prevent segment\n                correlation bias. The proposed model achieved 98.6% accuracy (precision = 98.26%,\n                recall = 98.95%, F1-score = 98.61%) on PhysioNet 2022, and 98.5% accuracy (precision\n                = 98.49%, recall = 98.52%, F1-score = 98.50%) on PhysioNet 2016, demonstrating\n                consistently high performance across datasets with different class distributions.\n                These results indicate that combining temporal modeling with balanced learning\n                improves robustness in murmur detection. The findings highlight the potential of\n                PCG-based deep learning systems to support scalable, non-invasive cardiac screening,\n                particularly in settings with limited access to specialist assessment.<\/jats:p>","DOI":"10.1038\/s41598-026-45276-9","type":"journal-article","created":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T05:37:10Z","timestamp":1775885830000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Advancing cardiovascular screening: deep\n            learning-based heart-sound classification using SMOTE and temporal\n            modeling"],"prefix":"10.1038","volume":"16","author":[{"given":"Asmaa","family":"Ameen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Eldesouky Fattoh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarek","family":"Abd El-Hafeez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kareem","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"45276_CR1","unstructured":"WHO. World Health Organization. Media\n                        centre-Cardiovascular Diseases (CVDs),. World Health\n                    Organization. Cardiovascular diseases (CVDs) Fact sheet. Retrieved 19 - Feb -\n                    2024 from\n                            http:\/\/www.who.int\/mediacentre\/factsheets\/fs317\/en\/ (2021)"},{"issue":"7","key":"45276_CR2","first-page":"e46","volume":"121","author":"WG Members","year":"2010","unstructured":"Members, W. G. et al. Heart disease and stroke statistics\u20142010\n                    update: a report from the American Heart Association. Circulation\n                    121(7), e46\u2013e215 (2010).","journal-title":"Circulation"},{"issue":"11","key":"45276_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11111962","volume":"11","author":"MA Kobat","year":"2021","unstructured":"Kobat, M. A. et al. Automated COVID-19 and heart failure detection\n                    using DNA pattern technique with cough sounds. Diagnostics\n                    11(11), 1962 (2021).","journal-title":"Diagnostics"},{"key":"45276_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1475-925X-10-13","volume":"10","author":"S Yuenyong","year":"2011","unstructured":"Yuenyong, S., Nishihara, A., Kongprawechnon, W. & Tungpimolrut,\n                    K. A framework for automatic heart sound analysis without segmentation.\n                        Biomed. Eng. Online\n                    10, 1\u201323 (2011).","journal-title":"Biomed. Eng. Online"},{"issue":"1","key":"45276_CR5","first-page":"11","volume":"46","author":"M Lam","year":"2005","unstructured":"Lam, M. et al. Factors influencing cardiac auscultation proficiency\n                    in physician trainees. Singapore Med. J.\n                    46(1), 11 (2005).","journal-title":"Singapore Med. J."},{"key":"45276_CR6","doi-asserted-by":"crossref","unstructured":"Strunic, S. L., Rios-Guti\u00e9rrez, F., Alba-Flores, R., Nordehn, G.,\n                    & Burns, S. Detection and classification of cardiac murmurs using\n                    segmentation techniques and artificial neural networks. 2007 IEEE symposium on\n                    computational intelligence and data mining (2007)","DOI":"10.1109\/CIDM.2007.368902"},{"key":"45276_CR7","doi-asserted-by":"crossref","unstructured":"Reyna, M. A., Kiarashi, Y., Elola, A., Oliveira, J., Renna, F., Gu,\n                    A., Alday, E. A. P., Sadr, N., Sharma, A., & Mattos, S. Heart murmur\n                    detection from phonocardiogram recordings: The george b. moody physionet\n                    challenge 2022. 2022 Computing in Cardiology (CinC) (2022)","DOI":"10.1101\/2022.08.11.22278688"},{"key":"45276_CR8","doi-asserted-by":"crossref","unstructured":"Vermarien, H. Phonocardiography. Encyclopedia of medical devices and instrumentation.\n                    (2006)","DOI":"10.1002\/0471732877.emd203"},{"issue":"7","key":"45276_CR9","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.cpcardiol.2008.03.003","volume":"33","author":"MA Chizner","year":"2008","unstructured":"Chizner, M. A. Cardiac auscultation: rediscovering the lost art.\n                        Curr. Probl. Cardiol.\n                    33(7), 326\u2013408 (2008).","journal-title":"Curr. Probl. Cardiol."},{"key":"45276_CR10","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2024.1434022","volume":"7","author":"E Partovi","year":"2024","unstructured":"Partovi, E., Babic, A. & Gharehbaghi, A. A review on deep\n                    learning methods for heart sound signal analysis. Front.\n                        Artif. Intell.\n                    7, 1434022 (2024).","journal-title":"Front. Artif. 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Their correct affiliation is only Affiliation 3: Computer Science Department, Faculty of Science, Minia University, Minia, Egypt.The original Article has been corrected.","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial\n                    interests or personal relationships that could have appeared to influence the\n                    work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12063"}}