{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T10:57:03Z","timestamp":1772967423267,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018556","name":"Suzhou Science and Technology Project","doi-asserted-by":"publisher","award":["SYG201906"],"award-info":[{"award-number":["SYG201906"]}],"id":[{"id":"10.13039\/501100018556","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Cooperation High-tech Industrialization Special Project funded by Jilin Province and the Chinese Academy of Sciences","award":["2020SYHZ0043"],"award-info":[{"award-number":["2020SYHZ0043"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification\/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.<\/jats:p>","DOI":"10.3390\/s21217163","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T23:52:35Z","timestamp":1635465155000},"page":"7163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors"],"prefix":"10.3390","volume":"21","author":[{"given":"Jun","family":"Zhong","sequence":"first","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"},{"name":"Suzhou Institute of Biomedical Engineering and Technology, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Hai","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1392-8348","authenticated-orcid":false,"given":"Changzhe","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2619-6481","authenticated-orcid":false,"given":"Shuiping","family":"Gou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenliang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e139","DOI":"10.1161\/CIR.0000000000000757","article-title":"Heart Disease and Stroke Statistics\u20142020 Update: A Report From the American Heart Association","volume":"141","author":"Virani","year":"2020","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Junaid, M.J.A., and Kumar, R. (2020, January 17\u201319). Data Science And Its Application In Heart Disease Prediction. Proceedings of the 2020 International Conference on Intelligent Engineering and Management, London, UK.","DOI":"10.1109\/ICIEM48762.2020.9160056"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ghaleb, F.A., Kamat, M., Salleh, M., Rohani, M.F., and Hadji, S.E. (2017, January 23\u201324). Motion Artifact Reduction Algorithm Using Sequential Adaptive Noise Filters and Estimation Methods for Mobile ECG. Proceedings of the International Conference of Reliable Information and Communication Technology, Johor Bahru, Malaysia.","DOI":"10.1007\/978-3-319-59427-9_13"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1109\/TITB.2009.2033053","article-title":"A Wearable ECG Acquisition System with Compact Planar-Fashionable Circuit Board-Based Shirt","volume":"13","author":"Yoo","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pham, T., Lau, Z.J., Chen, S.H.A., and Makowski, D. (2021). Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. Sensors, 21.","DOI":"10.20944\/preprints202105.0070.v1"},{"key":"ref_6","first-page":"9","article-title":"New Concepts and Technologies in Home Care and Ambulatory Monitoring","volume":"108","author":"Dittmar","year":"2004","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/1743-0003-2-17","article-title":"Wearable feedback systems for rehabilitation","volume":"2","author":"Sung","year":"2005","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.biopsycho.2007.04.001","article-title":"Accuracy of the LifeShirt\u00ae (Vivometrics) in the detection of cardiac rhythms","volume":"75","author":"Heilman","year":"2007","journal-title":"Biol. Psychol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/TITB.2010.2045003","article-title":"Smart Garments for Emergency Operators: The ProeTEX Project","volume":"14","author":"Curone","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.snb.2009.04.040","article-title":"Wireless Sensor Network based Wearable Smart Shirt for Ubiquitous Health and Activity Monitoring","volume":"140","author":"Lee","year":"2009","journal-title":"Sens. Actuators B Chem."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, C.C., Lin, S.Y., and Chang, W.Y. (2021). Novel Stable Capacitive Electrocardiogram Measurement System. Sensors, 21.","DOI":"10.3390\/s21113668"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cheng, J., Zhong, J., Wang, H., Tang, X., Jiao, C., and Zhou, H. (2020, January 17\u201319). Evolutionary Optimized Multiple Instance Concept Learning for Beat-to-Beat Heart Rate Estimation from Electrocardiograms. Proceedings of the 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Chengdu, China.","DOI":"10.1109\/CISP-BMEI51763.2020.9263655"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A Real-time QRS Detection Algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ramakrishnan, S., and Yogeswaran, R. (2017, January 6\u20138). Design and Analysis of Feature Extraction Algorithm for ECG Signals using Adaptive Threshold Method. Proceedings of the 2017 Trends in Industrial Measurement and Automation, Chennai, India.","DOI":"10.1109\/TIMA.2017.8064801"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Banerjee, S., and Mitra, M. (2010, January 16\u201318). ECG Feature Extraction and Classification of Anteroseptal Myocardial Infarction and Normal Subjects using Discrete Wavelet Transform. Proceedings of the 2010 International Conference on Systems in Medicine and Biology, Kharagpur, India.","DOI":"10.1109\/ICSMB.2010.5735345"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/10.740880","article-title":"Real-time Discrimination of Ventricular Tachyarrhythmia with Fourier-Transform Neural Network","volume":"46","author":"Minami","year":"1999","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2261-5-28","article-title":"Characteristic Wave Detection in ECG Signal using Morphological Transform","volume":"5","author":"Sun","year":"2005","journal-title":"BMC Cardiovasc. Disord."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1007\/s13534-011-0029-4","article-title":"Auto-Detection of R Wave in Electrocardiography for Patch-Type ECG Remote Monitoring System","volume":"1","author":"Kim","year":"2011","journal-title":"Biomed. Eng. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dewangan, N.K., and Shukla, S. (2016, January 20\u201321). ECG Arrhythmia Classification using Discrete Wavelet Transform and Artificial Neural Network. Proceedings of the 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, Bangalore, India.","DOI":"10.1109\/RTEICT.2016.7808164"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TBME.2004.824138","article-title":"Support Vector Machine-based Expert System for Reliable Heartbeat Recognition","volume":"51","author":"Osowski","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/10.740882","article-title":"ECG Beat Detection using Filter Banks","volume":"46","author":"Afonso","year":"1999","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1114\/1.1523030","article-title":"A Real Time QRS Detection Using Delay-Coordinate Mapping for the Microcontroller Implementation","volume":"30","author":"Lee","year":"2002","journal-title":"Ann. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sedghamiz, H., and Santonocito, D. (2015, January 19\u201321). Unsupervised Detection and Classification of Motor Unit Action Potentials in Intramuscular Electromyography Signals. Proceedings of the 2015 E-Health and Bioengineering Conference, Iasi, Romania.","DOI":"10.1109\/EHB.2015.7391510"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"588","DOI":"10.3390\/a5040588","article-title":"An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals","volume":"5","author":"Scholkmann","year":"2012","journal-title":"Algorithms"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/TBME.2016.2549060","article-title":"QRS Detection Algorithm for Telehealth Electrocardiogram Recordings","volume":"63","author":"Khamis","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.compbiomed.2018.06.002","article-title":"Automated Diagnosis of Arrhythmia using Combination of CNN and LSTM Techniques with Variable Length Heart Beats","volume":"102","author":"Oh","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fotiadou, E., Xu, M., van Erp, B., van Sloun, R.J., and Vullings, R. (2020, January 20\u201324). Deep Convolutional Long Short-Term Memory Network for Fetal Heart Rate Extraction. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175442"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lee, J.S., Seo, M., Kim, S.W., and Choi, M. (2018, January 24\u201327). Fetal QRS detection based on convolutional neural networks in noninvasive fetal electrocardiogram. Proceedings of the 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), Poitiers, France.","DOI":"10.1109\/ICFSP.2018.8552074"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s11760-014-0709-5","article-title":"Advanced K-means clustering algorithm for large ECG data sets based on a collaboration of compressed sensing theory and K-SVD approach","volume":"10","author":"Balouchestani","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Annam, J.R., and Surampudi, B.R. (2016, January 22\u201324). AAMI Based ECG Heart-Beat Time-Series Clustering Using Unsupervised ELM and Decision Rule. Proceedings of the 2016 International Conference on Information Technology, Bhubaneswar, India.","DOI":"10.1109\/ICIT.2016.039"},{"key":"ref_31","unstructured":"Zazula, D. (September, January 28). Optimization of Heartbeat Detection based on Clustering and Multimethod Approach. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum Likelihood from Incomplete Data via the EM Algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A Dendrite Method for Cluster Analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun.-Stat.-Theory Methods"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2634","DOI":"10.1109\/TBME.2018.2812602","article-title":"Multiple instance dictionary learning for beat-to-beat heart rate monitoring from ballistocardiograms","volume":"65","author":"Jiao","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"671","DOI":"10.21105\/joss.00671","article-title":"BioSigKit: A Matlab Toolbox and Interface for Analysis of Biosignals","volume":"3","author":"Sedghamiz","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"193","DOI":"10.3233\/AIS-170423","article-title":"Heart Rate Monitoring using Hydraulic Bed Sensor Ballistocardiogram","volume":"9","author":"Rosales","year":"2017","journal-title":"J. Ambient. Intell. Smart Environ."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:22:01Z","timestamp":1760167321000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,28]]},"references-count":37,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217163"],"URL":"https:\/\/doi.org\/10.3390\/s21217163","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,28]]}}}