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While several public ECG\u2013PPG datasets exist, they lack the diversity found in image datasets, and the data collection process often introduces noise, making ECG reconstruction from PPG signals challenging even for advanced machine learning models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose a novel ODE-based method for generating synthetic ECG\u2013PPG pairs to enhance training diversity. Building on this, we introduce CLEP-GAN, a subject-independent PPG-to-ECG reconstruction framework that integrates contrastive learning, adversarial learning, and attention gating. CLEP-GAN achieves performance that matches or surpasses current state-of-the-art methods, particularly in reconstructing ECG signals from unseen subjects. Evaluation on real-world datasets (BIDMC and CapnoBase) confirms its effectiveness. Additionally, our analysis shows that demographic factors such as sex and age significantly impact reconstruction accuracy, emphasizing the importance of incorporating demographic diversity during model training and data augmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our method produces synthetic ECG\u2013PPG pairs with RR interval distributions closely aligned with their real counterparts and shows strong potential to simulate diverse rhythms such as regular sinus rhythm (RSR), sinus arrhythmia (SA), and atrial fibrillation (AFib). Furthermore, CLEP-GAN demonstrates robust performance on both synthetic and real datasets, achieving near-perfect reconstruction in synthetic settings and competitive results on real data. These findings highlight CLEP-GAN\u2019s promise for reliable, non-invasive ECG monitoring in clinical applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-025-06276-0","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T07:35:20Z","timestamp":1764056120000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["CLEP-GAN: an innovative approach to subject-independent ECG reconstruction from PPG signals"],"prefix":"10.1186","volume":"26","author":[{"given":"Xiaoyan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal","family":"Habib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neda","family":"Aminnejad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arvind","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaxiong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,25]]},"reference":[{"issue":"9","key":"6276_CR1","doi-asserted-by":"publisher","first-page":"8140","DOI":"10.1109\/JIOT.2022.3231862","volume":"10","author":"X Tian","year":"2023","unstructured":"Tian X, Zhu Q, Li Y, Wu M. 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The CapnoBase dataset was collected at BC Children\u2019s Hospital and St. Paul\u2019s Hospital, Vancouver, BC, under approval of the University of British Columbia Clinical Research Ethics Board (UBC CREB), as described by Karlen et al.\u00a0[\n                      \n                      ], and is released in anonymized form in compliance with data privacy guidelines. The BIDMC dataset was collected from critically ill adult patients at Beth Israel Deaconess Medical Center and released via PhysioNet\/MIMIC-II under the oversight of the Committee on Clinical Investigations, Institutional Review Board at BIDMC, with all data de-identified in accordance with HIPAA standards. The original dataset publications do not specify a protocol number. This work represents a secondary analysis of these de-identified, publicly available datasets and was conducted in accordance with institutional requirements and recognized ethical principles, including the Declaration of Helsinki. Human Ethics and Consent to Participate declarations: not applicable, as no direct recruitment of human participants was performed. Clinical trial number: not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no Competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Cmpeting interests"}}],"article-number":"306"}}