{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:44:39Z","timestamp":1771703079335,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single\u2013lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats\/signals from the same subject\u2019s record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects\u2019 records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats\/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted\/cleaned from the MIMIC II dataset.<\/jats:p>","DOI":"10.3390\/info14070377","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T01:38:32Z","timestamp":1688434712000},"page":"377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Subject-Independent per Beat PPG to Single-Lead ECG Mapping"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7245-7155","authenticated-orcid":false,"given":"Khaled M.","family":"Abdelgaber","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag 82524, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mostafa","family":"Salah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag 82524, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9302-7875","authenticated-orcid":false,"given":"Osama A.","family":"Omer","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4814-8840","authenticated-orcid":false,"given":"Ahmed E. A.","family":"Farghal","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag 82524, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6375-4243","authenticated-orcid":false,"given":"Ahmed S.","family":"Mubarak","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,3]]},"reference":[{"key":"ref_1","unstructured":"Organization World Health (2022). 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