{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:56:15Z","timestamp":1777895775679,"version":"3.51.4"},"reference-count":59,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Photoplethysmographic (PPG) signals are mainly employed for heart rate estimation but are also fascinating candidates in the search for cardiovascular biomarkers. However, their high susceptibility to motion artifacts can lower their morphological quality and, hence, affect the reliability of the extracted information. Low reliability is particularly relevant when signals are recorded in a real-world context, during daily life activities. We aim to develop two classifiers to identify PPG pulses suitable for heart rate estimation (Basic-quality classifier) and morphological analysis (High-quality classifier). We collected wrist PPG data from 31 participants over a 24 h period. We defined four activity ranges based on accelerometer data and randomly selected an equal number of PPG pulses from each range to train and test the classifiers. Independent raters labeled the pulses into three quality levels. Nineteen features, including nine novel features, were extracted from PPG pulses and accelerometer signals. We conducted ten-fold cross-validation on the training set (70%) to optimize hyperparameters of five machine learning algorithms and a neural network, and the remaining 30% was used to test the algorithms. Performances were evaluated using the full features and a reduced set, obtained downstream of feature selection methods. Best performances for both Basic- and High-quality classifiers were achieved using a Support Vector Machine (Acc: 0.96 and 0.97, respectively). Both classifiers outperformed comparable state-of-the-art classifiers. Implementing automatic signal quality assessment methods is essential to improve the reliability of PPG parameters and broaden their applicability in a real-world context.<\/jats:p>","DOI":"10.3390\/s22155831","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T02:12:39Z","timestamp":1659665559000},"page":"5831","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0538-650X","authenticated-orcid":false,"given":"Serena","family":"Moscato","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic, and Information Engineering \u201cGuglielmo Marconi\u201d\u2014DEI, University of Bologna, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stella","family":"Lo Giudice","sequence":"additional","affiliation":[{"name":"School of Engineering (Digital Technology Engineering), Pulsed Academy, Fontys University of Applied Science, 5612 MA Eindhoven, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1227-5787","authenticated-orcid":false,"given":"Giulia","family":"Massaro","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-4370","authenticated-orcid":false,"given":"Lorenzo","family":"Chiari","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic, and Information Engineering \u201cGuglielmo Marconi\u201d\u2014DEI, University of Bologna, 40136 Bologna, Italy"},{"name":"Health Sciences and Technologies\u2014Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, 40136 Bologna, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"ref_1","first-page":"100","article-title":"A Review on Wearable Photoplethysmography Sensors and Their Potential Future Applications in Health Care","volume":"4","author":"Castaneda","year":"2018","journal-title":"Int. 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