{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T08:39:57Z","timestamp":1780043997241,"version":"3.53.1"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,20]],"date-time":"2021-03-20T00:00:00Z","timestamp":1616198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["NRF-2018R1A4A1025704"],"award-info":[{"award-number":["NRF-2018R1A4A1025704"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["NRF-2016M 3A9F1941328"],"award-info":[{"award-number":["NRF-2016M 3A9F1941328"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as \u2018good\u2019 and \u2018poor\u2019 classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a \u2018poor\u2019 class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require complicated pre-processing or a feature detection process.<\/jats:p>","DOI":"10.3390\/s21062188","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T23:47:41Z","timestamp":1616370461000},"page":"2188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8832-3618","authenticated-orcid":false,"given":"Donggeun","family":"Roh","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Chonnam National University, 50 Daehak-ro, Yeosu 59626, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8095-356X","authenticated-orcid":false,"given":"Hangsik","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Chonnam National University, 50 Daehak-ro, Yeosu 59626, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/28\/3\/R01","article-title":"Photoplethysmography and its application in clinical physiological measurement","volume":"28","author":"Allen","year":"2007","journal-title":"Physiol. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1088\/0967-3334\/31\/5\/008","article-title":"The non-invasive and continuous estimation of cardiac output using a photoplethysmogram and electrocardiogram during incremental exercise","volume":"31","author":"Wang","year":"2010","journal-title":"Physiol. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1161\/01.HYP.32.2.365","article-title":"Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform","volume":"32","author":"Takazawa","year":"1998","journal-title":"Hypertension"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s12938-016-0302-y","article-title":"Feasibility study for the non-invasive blood pressure estimation based on ppg morphology: Normotensive subject study","volume":"16","author":"Shin","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1364\/BOE.7.003007","article-title":"Optical blood pressure estimation with photoplethysmography and FFT-based neural networks","volume":"7","author":"Xing","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"141970","DOI":"10.1109\/ACCESS.2019.2942936","article-title":"Cuffless Continuous Blood Pressure Estimation from Pulse Morphology of Photoplethysmograms","volume":"7","author":"Yan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1093\/bja\/aem004","article-title":"Assessment of surgical stress during general anaesthesia","volume":"98","author":"Huiku","year":"2007","journal-title":"Br. J. Anaesth."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/JBHI.2018.2890482","article-title":"Postoperative Pain Assessment Model Based on Pulse Contour Characteristics Analysis","volume":"23","author":"Seok","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s11818-017-0115-7","article-title":"Extended algorithm for real-time pulse waveform segmentation and artifact detection in photoplethysmograms","volume":"21","author":"Fischer","year":"2017","journal-title":"Somnologie"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1088\/0967-3334\/32\/3\/008","article-title":"Signal quality measures for pulse oximetry through waveform morphology analysis","volume":"32","author":"Sukor","year":"2011","journal-title":"Physiol. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Selvaraj, N., Mendelson, Y., Shelley, K.H., Silverman, D.G., and Chon, K.H. (September, January 30). Statistical approach for the detection of motion\/noise artifacts in Photoplethysmogram. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091232"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Elgendi, M. (2016). Optimal signal quality index for photoplethysmogram signals. Bioengineering, 3.","DOI":"10.3390\/bioengineering3040021"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.bspc.2018.05.020","article-title":"PQR signal quality indexes: A method for real-time photoplethysmogram signal quality estimation based on noise interferences","volume":"47","author":"Song","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_14","first-page":"832","article-title":"Signal-quality indices for the electrocardiogram and photoplethysmogram: Derivation and applications to wireless monitoring","volume":"19","author":"Orphanidou","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1088\/0967-3334\/33\/9\/1491","article-title":"Dynamic time warping and machine learning for signal quality assessment of pulsatile signals","volume":"33","author":"Li","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115007","DOI":"10.1088\/1361-6579\/aae7f8","article-title":"Sinus or not: A new beat detection algorithm based on a pulse morphology quality index to extract normal sinus rhythm beats from wrist-worn photoplethysmography recordings","volume":"39","author":"Papini","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, S.-H., Wang, J.-J., Chen, W., Pan, K.-L., and Su, C.-H. (2020). Classification of photoplethysmographic signal quality with fuzzy neural network for improvement of stroke volume measurement. Appl. Sci., 10.","DOI":"10.3390\/app10041476"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, S.-H., Li, R.-X., Wang, J.-J., Chen, W., and Su, C.-H. (2020). Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume. Appl. Sci., 10.","DOI":"10.3390\/app10134612"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.procs.2019.04.074","article-title":"A Real-time PPG quality assessment approach for healthcare internet-of-things","volume":"151","author":"Naeini","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1152\/jappl.1994.76.2.965","article-title":"Dynamical assessment of physiological systems and states using recurrence plot strategies","volume":"76","author":"Zbilut","year":"1994","journal-title":"J. Appl. Physiol."},{"key":"ref_21","first-page":"441","article-title":"Recurrence plots of dynamical systems","volume":"16","author":"Eckmann","year":"1995","journal-title":"World Sci. Ser. Nonlinear Sci. Ser. A"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1088\/0967-3334\/32\/8\/010","article-title":"Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal","volume":"32","author":"Mohebbi","year":"2011","journal-title":"Physiol. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1142\/S0129065711002808","article-title":"Application of recurrence quantification analysis for the automated identification of epileptic EEG signals","volume":"21","author":"Acharya","year":"2011","journal-title":"Int. J. Neural Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"255","DOI":"10.3389\/fphys.2019.00255","article-title":"Computer-aided diagnosis system of fetal hypoxia incorporating recurrence plot with convolutional neural network","volume":"10","author":"Zhao","year":"2019","journal-title":"Front. Physiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_26","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"Volume 25","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_27","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_28","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_29","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:38:41Z","timestamp":1760161121000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,20]]},"references-count":29,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21062188"],"URL":"https:\/\/doi.org\/10.3390\/s21062188","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,20]]}}}