{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:14:52Z","timestamp":1774455292230,"version":"3.50.1"},"reference-count":39,"publisher":"IOP Publishing","issue":"12","license":[{"start":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T00:00:00Z","timestamp":1577404800000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T00:00:00Z","timestamp":1577404800000},"content-version":"tdm","delay-in-days":26,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2019,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    : Photoplethysmography (PPG) monitoring has been implemented in many portable and wearable devices we use daily for health and fitness tracking. Its simplicity and cost-effectiveness has enabled a variety of biomedical applications, such as continuous long-term monitoring of heart arrhythmias, fitness, and sleep tracking, and hydration monitoring. One major issue that can hinder PPG-based applications is movement artifacts, which can lead to false interpretations. In many implementations, noisy PPG signals are discarded. Misinterpreted or discarded PPG signals pose a problem in applications where the goal is to increase the yield of detecting physiological events, such as in the case of paroxysmal atrial fibrillation (AF)\u2014a common episodic heart arrhythmia and a leading risk factor for stroke. In this work, we compared a traditional machine learning and deep learning approaches for PPG quality assessment in the presence of AF, in order to find the most robust method for PPG quality assessment.\n                    <jats:italic>Approach<\/jats:italic>\n                    : The training data set was composed of 78\u2009278 30\u2009s long PPG recordings from 3764 patients using bedside patient monitors. Two different representations of PPG signals were employed\u2014a time-series based (1D) one and an image-based (2D) one. Trained models were tested on an independent set of 2683 30\u2009s PPG signals from 13 stroke patients.\n                    <jats:italic>Main results<\/jats:italic>\n                    : ResNet18 showed a higher performance (0.985 accuracy, 0.979 specificity, and 0.988 sensitivity) than SVM and other deep learning approaches. 2D-based models were generally more accurate than 1D-based models.\n                    <jats:italic>Significance<\/jats:italic>\n                    : 2D representation of PPG signal enhances the accuracy of PPG signal quality assessment.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ab5b84","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T18:15:45Z","timestamp":1574705745000},"page":"125002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":46,"title":["Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation"],"prefix":"10.1088","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1681-2436","authenticated-orcid":false,"given":"Tania","family":"Pereira","sequence":"first","affiliation":[]},{"given":"Cheng","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Kais","family":"Gadhoumi","sequence":"additional","affiliation":[]},{"given":"Nate","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Rene A","family":"Colorado","sequence":"additional","affiliation":[]},{"given":"Karl","family":"Meisel","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Hu","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2019,12,27]]},"reference":[{"key":"pmeaab5b84bib001","article-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems","author":"Abadi","year":"2019","type":"web-resource"},{"key":"pmeaab5b84bib002","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/TITB.2009.2034845","type":"journal-article","article-title":"A robust approach toward recognizing valid arterial-blood-pressure pulses","volume":"14","author":"Asgari","year":"2010","journal-title":"IEEE Trans. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2019-09-03","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-11-25","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-12-27","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}