{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:45:13Z","timestamp":1772729113902,"version":"3.50.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004504","name":"Lietuvos Mokslo Taryba","doi-asserted-by":"publisher","award":["01.2.2-LMT-K-718-01-0030"],"award-info":[{"award-number":["01.2.2-LMT-K-718-01-0030"]}],"id":[{"id":"10.13039\/501100004504","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016248","name":"Kauno Technologijos Universitetas","doi-asserted-by":"publisher","award":["PP59\/208"],"award-info":[{"award-number":["PP59\/208"]}],"id":[{"id":"10.13039\/501100016248","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:p><jats:bold>Objective:<\/jats:bold> To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG).<\/jats:p><jats:p><jats:bold>Approach:<\/jats:bold> The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases. Manually annotated real PPG signals from the PhysioNet\/CinC 2015 Challenge Database are used for performance evaluation. The performance is compared to that of a pulse-based reference detector.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The sensitivity\/specificity were found to be 98.1%\/97.9 and 76.6%\/96.8% for the CNN-based detector, respectively, whereas the corresponding results for the pulse-based detector were 94.7%\/99.8 and 67.1%\/93.8%, respectively.<\/jats:p><jats:p><jats:bold>Significance:<\/jats:bold> The proposed detector may be useful for continuous, long-term monitoring of bradycardia and tachycardia using wearable devices, e.g., wrist-worn devices, especially in situations where sensitivity is favored over specificity. The study demonstrates that simulated PPG signals are suitable for training and validation of a CNN.<\/jats:p>","DOI":"10.3389\/fphys.2022.928098","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T06:18:56Z","timestamp":1658125136000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection"],"prefix":"10.3389","volume":"13","author":[{"given":"Andrius","family":"Solo\u0161enko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Birut\u0117","family":"Paliakait\u0117","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vaidotas","family":"Marozas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leif","family":"S\u00f6rnmo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/tbme.2005.855725","article-title":"An Automatic Beat Detection Algorithm for Pressure Signals","volume":"52","author":"Aboy","year":"2005","journal-title":"IEEE Trans. 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