{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T11:06:55Z","timestamp":1783595215799,"version":"3.55.0"},"reference-count":64,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T00:00:00Z","timestamp":1660348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["316810"],"award-info":[{"award-number":["316810"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["316811"],"award-info":[{"award-number":["316811"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["CNS-1831918"],"award-info":[{"award-number":["CNS-1831918"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. National Science Foundation (NSF)","award":["316810"],"award-info":[{"award-number":["316810"]}]},{"name":"U.S. National Science Foundation (NSF)","award":["316811"],"award-info":[{"award-number":["316811"]}]},{"name":"U.S. National Science Foundation (NSF)","award":["CNS-1831918"],"award-info":[{"award-number":["CNS-1831918"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.<\/jats:p>","DOI":"10.3390\/s22166054","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:44:03Z","timestamp":1660607043000},"page":"6054","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Robust PPG Peak Detection Using Dilated Convolutional Neural Networks"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0919-8661","authenticated-orcid":false,"given":"Kianoosh","family":"Kazemi","sequence":"first","affiliation":[{"name":"Department of Computing, Faculty of Technology, University of Turku, 20014 Turku, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8420-2173","authenticated-orcid":false,"given":"Juho","family":"Laitala","sequence":"additional","affiliation":[{"name":"Department of Computing, Faculty of Technology, University of Turku, 20014 Turku, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5003-299X","authenticated-orcid":false,"given":"Iman","family":"Azimi","sequence":"additional","affiliation":[{"name":"Department of Computing, Faculty of Technology, University of Turku, 20014 Turku, Finland"},{"name":"Department of Computer Science, University of California, Irvine, CA 92697-3435, USA"},{"name":"Institute for Future Health, University of California, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9392-3589","authenticated-orcid":false,"given":"Pasi","family":"Liljeberg","sequence":"additional","affiliation":[{"name":"Department of Computing, Faculty of Technology, University of Turku, 20014 Turku, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0725-1155","authenticated-orcid":false,"given":"Amir M.","family":"Rahmani","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California, Irvine, CA 92697-3435, USA"},{"name":"Institute for Future Health, University of California, Irvine, CA 92697, USA"},{"name":"School of Nursing, University of California, Irvine, CA 92697, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1016\/j.future.2017.09.016","article-title":"Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics","volume":"78","author":"Firouzi","year":"2018","journal-title":"Future Gener. 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