{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:49:48Z","timestamp":1760244588371,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["PD\/BDE\/150312\/2019","UIDB\/00066\/2020"],"award-info":[{"award-number":["PD\/BDE\/150312\/2019","UIDB\/00066\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CTS Research Unit\u2014Center of Technology and Systems\u2014UNINOVA","award":["PD\/BDE\/150312\/2019","UIDB\/00066\/2020"],"award-info":[{"award-number":["PD\/BDE\/150312\/2019","UIDB\/00066\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Heart Rate Variability (HRV) is a biomarker that can be obtained non-invasively from the electrocardiogram (ECG) or the photoplethysmogram (PPG) fiducial points. However, the accuracy of HRV can be compromised by the presence of artifacts. In the herein presented work, a Simulink\u00ae model with a deep learning component was studied for overly noisy PPG signals. A subset with these noisy signals was selected for this study, with the purpose of testing a real-time machine learning based HRV estimation system in substandard artifact-ridden signals. Home-based and wearable HRV systems are prone to dealing with higher contaminated signals, given the less controlled environment where the acquisitions take place, namely daily activity movements. This was the motivation behind this work. The results for overly noisy signals show that the real-time PPG-based HRV estimation system produced RMSE and Pearson correlation coefficient mean and standard deviation of 0.178 \u00b1 0.138 s and 0.401 \u00b1 0.255, respectively. This RMSE value is roughly one order of magnitude above the closest comparative results for which the real-time system was also used.<\/jats:p>","DOI":"10.3390\/computers11120177","type":"journal-article","created":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T02:55:39Z","timestamp":1670295339000},"page":"177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["On the Feasibility of Real-Time HRV Estimation Using Overly Noisy PPG Signals"],"prefix":"10.3390","volume":"11","author":[{"given":"Filipa","family":"Esgalhado","sequence":"first","affiliation":[{"name":"Laboratory of Instrumentation, Biomedical Engineering and Radiation Physics (LIBPHYS), NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"},{"name":"NMT, S.A., Parque Tecnol\u00f3gico de Cantanhede, N\u00facleo 04, Lote 3, 3060-197 Cantanhede, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7913-7047","authenticated-orcid":false,"given":"Valentina","family":"Vassilenko","sequence":"additional","affiliation":[{"name":"Laboratory of Instrumentation, Biomedical Engineering and Radiation Physics (LIBPHYS), NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"},{"name":"NMT, S.A., Parque Tecnol\u00f3gico de Cantanhede, N\u00facleo 04, Lote 3, 3060-197 Cantanhede, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2287-4265","authenticated-orcid":false,"given":"Arnaldo","family":"Batista","sequence":"additional","affiliation":[{"name":"UNINOVA CTS, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4270-3284","authenticated-orcid":false,"given":"Manuel","family":"Ortigueira","sequence":"additional","affiliation":[{"name":"UNINOVA CTS, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1001\/jama.2021.5469","article-title":"The Leading Causes of Death in the US for 2020","volume":"325","author":"Ahmad","year":"2021","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.15420\/aer.2018.27.2","article-title":"Heart Rate Variability: An Old Metric with New Meaning in the Era of using mHealth Technologies for Health and Exercise Training Guidance. Part One: Physiology and Methods","volume":"7","author":"Singh","year":"2018","journal-title":"Arrhythmia Electrophysiol. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/oxfordjournals.eurheartj.a014868","article-title":"Heart rate variability Standards of measurement, physiological interpretation, and clinical use","volume":"17","author":"Malik","year":"1996","journal-title":"Eur. Heart J."},{"key":"ref_4","first-page":"299","article-title":"Heart rate variability and myocardial infarction: Systematic literature review and metanalysis","volume":"13","author":"Buccelletti","year":"2009","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/0002-9149(87)90795-8","article-title":"Decreased heart rate variability and its association with increased mortality after acute myocardial infarction","volume":"59","author":"Kleiger","year":"1987","journal-title":"Am. J. Cardiol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Esgalhado, F., Batista, A., Vassilenko, V., Russo, S., and Ortigueira, M. (2022). Peak Detection and HRV Feature Evaluation on ECG and PPG Signals. Symmetry, 14.","DOI":"10.3390\/sym14061139"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1080\/03091900701781317","article-title":"Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography","volume":"32","author":"Selvaraj","year":"2008","journal-title":"J. Med. Eng. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10877-007-9103-y","article-title":"Can Photoplethysmography Variability Serve as an Alternative Approach to Obtain Heart Rate Variability Information?","volume":"22","author":"Lu","year":"2008","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"960","DOI":"10.30773\/pi.2020.0168","article-title":"Heart Rate Variability Analysis: How Much Artifact Can We Remove?","volume":"17","author":"Sheridan","year":"2020","journal-title":"Psychiatry Investig."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hinde, K., White, G., and Armstrong, N. (2021). Wearable Devices Suitable for Monitoring Twenty Four Hour Heart Rate Variability in Military Populations. Sensors, 21.","DOI":"10.3390\/s21041061"},{"key":"ref_11","first-page":"7","article-title":"Can Wearable Devices Accurately Measure Heart Rate Variability? A Systematic Review","volume":"60","author":"Georgiou","year":"2018","journal-title":"Folia Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alfred, R., Iida, H., Haviluddin, H., and Anthony, P. (2021). Computational Science and Technology, Springer. Lecture Notes in Electrical Engineering.","DOI":"10.1007\/978-981-33-4069-5"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Vicente, A., Hernando, D., Mar\u00edn-Puyalto, J., Vicente-Rodr\u00edguez, G., Garatachea, N., Pueyo, E., and Bail\u00f3n, R. (2021). Validity of the Polar H7 Heart Rate Sensor for Heart Rate Variability Analysis during Exercise in Different Age, Body Composition and Fitness Level Groups. Sensors, 21.","DOI":"10.3390\/s21030902"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"948704","DOI":"10.3389\/fnagi.2022.948704","article-title":"CNNG: A Convolutional Neural Networks With Gated Recurrent Units for Autism Spectrum Disorder Classification","volume":"14","author":"Jiang","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103239","DOI":"10.1016\/j.bspc.2021.103239","article-title":"Attention deficit\/hyperactivity disorder Classification based on deep spatio-temporal features of functional Magnetic Resonance Imaging","volume":"71","author":"Liu","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kazemi, K., Laitala, J., Azimi, I., Liljeberg, P., and Rahmani, A.M. (2022). Robust PPG Peak Detection Using Dilated Convolutional Neural Networks. Sensors, 22.","DOI":"10.3390\/s22166054"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105596","DOI":"10.1016\/j.cmpb.2020.105596","article-title":"Robust PPG motion artifact detection using a 1-D convolution neural network","volume":"196","author":"Goh","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, H., Jimison, H., and Pavel, M. (2021, January 26\u201330). Reducing Motion Artifacts of Pulse Intervals from Photoplethysmogram of a Commercial Wristband for Heart Rate Variability Analysis. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Guadalajara, Mexico.","DOI":"10.1109\/EMBC46164.2021.9630551"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.jelectrocard.2017.08.020","article-title":"Efficient noise-tolerant estimation of heart rate variability using single-channel photoplethysmography","volume":"50","author":"Firoozabadi","year":"2017","journal-title":"J. Electrocardiol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Salehizadeh, S., Dao, D., Bolkhovsky, J., Cho, C., Mendelson, Y., and Chon, K. (2015). A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor. Sensors, 16.","DOI":"10.3390\/s16010010"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yap, J.H., and Jeong, D.U. (2013). Design and Implementation of Ubiquitous ECG Monitoring System by Using Android Tablet. Ubiquitous Information Technologies and Applications, Springer.","DOI":"10.1007\/978-94-007-5857-5_29"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, B.-H., Noh, Y.-H., and Jeong, D.-U. (2015, January 24\u201327). A Wearable ECG Monitoring System Using Adaptive EMD Filter Based on Activity Status. Proceedings of the 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, Gwangiu, Korea.","DOI":"10.1109\/WAINA.2015.73"},{"key":"ref_23","unstructured":"Allen, J., and Murray, A. (2004, January 19\u201322). Effects of filtering on multi-site photoplethysmography pulse waveform characteristics. Proceedings of the Computers in Cardiology, Chicago, IL, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Esgalhado, F., Fernandes, B., Vassilenko, V., Batista, A., and Russo, S. (2021). The Application of Deep Learning Algorithms for PPG Signal Processing and Classification. Computers, 10.","DOI":"10.3390\/computers10120158"},{"key":"ref_25","first-page":"195","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. J. Biosens. Bioelectron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/1475-925X-13-50","article-title":"Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter","volume":"13","author":"Peng","year":"2014","journal-title":"Biomed. Eng. Online"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Casadei, B.C., Gumiero, A., Tantillo, G., Della Torre, L., and Olmo, G. (2022). Systolic Blood Pressure Estimation from PPG Signal Using ANN. Electron., 11.","DOI":"10.3390\/electronics11182909"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Esgalhado, F., Batista, A., Vassilenko, V., and Ortigueira, M. (2022). Real-Time PPG-Based HRV Implementation Using Deep Learning and Simulink. IFIP Advances in Information and Communication Technology, Springer.","DOI":"10.1007\/978-3-031-07520-9_10"},{"key":"ref_29","unstructured":"Luk\u00e1\u010d, T., and Ondr\u00e1\u010dek, O. (2012). Using Simulink and Matlab for Real-Time ECG Signal Processing. Conf. MATLAB."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/12\/177\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:34:47Z","timestamp":1760146487000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/12\/177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,6]]},"references-count":29,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["computers11120177"],"URL":"https:\/\/doi.org\/10.3390\/computers11120177","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2022,12,6]]}}}