{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:40:39Z","timestamp":1780342839470,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"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>Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.<\/jats:p>","DOI":"10.3390\/s22145080","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T21:15:52Z","timestamp":1657142152000},"page":"5080","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Heartbeat Classifier for Continuous Prediction Using a Wearable Device"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8557-0093","authenticated-orcid":false,"given":"Eko Sakti","family":"Pramukantoro","sequence":"first","affiliation":[{"name":"Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 3-1-1 Tsushimanaka, Kita-Ku, Okayama 700-8530, Japan"},{"name":"Faculty of Computer Science, Brawijaya University, Malang 65145, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akio","family":"Gofuku","sequence":"additional","affiliation":[{"name":"Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 3-1-1 Tsushimanaka, Kita-Ku, Okayama 700-8530, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"ref_1","first-page":"60","article-title":"Cardiovascular diseases","volume":"Volume 2","author":"Charlton","year":"1997","journal-title":"The Health of Adult Britain 1841\u20131994"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1007\/s00421-019-04142-5","article-title":"RR interval signal quality of a heart rate monitor and an ECG Holter at rest and during exercise","volume":"119","author":"Schweizer","year":"2019","journal-title":"Eur. J. Appl. Physiol."},{"key":"ref_3","unstructured":"Polar Electro (2019). Polar H10 Heart Rate Sensor System. Polar Res. Technol., 1, 6\u201311."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pramukantoro, E.S., and Gofuku, A. (2021, January 5\u201311). A study of bluetooth low energy (BLE) frameworks on the IoT based heart monitoring system. Proceedings of the 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), Nara, Japan.","DOI":"10.1109\/LifeTech52111.2021.9391777"},{"key":"ref_5","unstructured":"Polar Electro (2021, July 05). Let\u2019s build products together. Available online: https:\/\/www.polar.com\/en\/developers."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pramukantoro, E.S., and Gofuku, A. (2021, January 13\u201314). A study of Real-Time HRV Analysis Using a Commercial Wearable Device. Proceedings of the 6th International Conference on Sustainable Information Engineering and Technology 2021, Malang, Indonesia.","DOI":"10.1145\/3479645.3479677"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.cmpb.2015.12.008","article-title":"ECG-based heartbeat classification for arrhythmia detection: A survey","volume":"127","author":"Luz","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"192727","DOI":"10.1109\/ACCESS.2020.3033004","article-title":"Predicting hypertensive patients with higher risk of developing vascular events using heart rate variability and machine learning","volume":"8","author":"Alkhodari","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6769","DOI":"10.1038\/s41598-020-63566-8","article-title":"Application of a convolutional neural network for predicting the occurrence of ventricular tachyarrhythmia using heart rate variability features","volume":"10","author":"Taye","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_10","first-page":"390","article-title":"Measuring heart rate variability using commercially available devices in healthy children: A validity and reliability study","volume":"10","author":"Speer","year":"2020","journal-title":"Eur. J. Investig. Heal. Psychol. Educ."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the MIT-BIH Arrhythmia Database","volume":"20","author":"Mark","year":"2001","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_13","first-page":"1","article-title":"Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning","volume":"18","author":"Nogueira","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_14","unstructured":"(1998). Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms (Standard No. AAMI EC57)."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.jacc.2015.04.062","article-title":"Ventricular Ectopy as a Predictor of Heart Failure and Death","volume":"66","author":"Dukes","year":"2015","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1016\/j.amjcard.2015.09.025","article-title":"Frequent Atrial Premature Complexes and Their Association with Risk of Atrial Fibrillation","volume":"116","author":"Acharya","year":"2015","journal-title":"Am. J. Cardiol."},{"key":"ref_17","first-page":"100033","article-title":"A review on deep learning methods for ECG arrhythmia classification","volume":"7","author":"Ebrahimi","year":"2020","journal-title":"Expert Syst. Appl. X"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rodrigues, P.R.F., da Silva Monteiro Filho, J.M., and do Vale Madeiro, J.P. (2018). The issue of automatic classification of heartbeats. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition, Academic Press.","DOI":"10.1016\/B978-0-12-814035-2.00013-X"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lin, C.C., and Yang, C.M. (2014). Heartbeat classification using normalized RR intervals and morphological features. Math. Probl. Eng.","DOI":"10.1109\/IS3C.2014.175"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.artmed.2004.03.007","article-title":"An arrhythmia classification system based on the RR-interval signal","volume":"33","author":"Tsipouras","year":"2005","journal-title":"Artif. Intell. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1016\/j.amjcard.2011.01.028","article-title":"A simple method to detect atrial fibrillation using RR intervals","volume":"107","author":"Lian","year":"2011","journal-title":"Am. J. Cardiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1587\/transinf.2017EDP7285","article-title":"ECG-Based heartbeat classification using two-level convolutional neural network and RR interval difference","volume":"E101D","author":"Xiang","year":"2018","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.future.2018.03.057","article-title":"A deep learning approach for ECG-based heartbeat classification for arrhythmia detection","volume":"86","author":"Sannino","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s42452-021-04185-4","article-title":"Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA","volume":"3","author":"Tyagi","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saenz-Cogollo, J.F., and Agelli, M. (2020). Investigating feature selection and random forests for inter-patient heartbeat classification. Algorithms, 13.","DOI":"10.20944\/preprints202003.0036.v1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.bspc.2018.08.007","article-title":"Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers","volume":"47","author":"Novo","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TBME.2004.827359","article-title":"Automatic classification of heartbeats using ECG morphology and heartbeat interval features","volume":"51","author":"Reilly","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_29","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2021, September 07). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.future.2019.03.025","article-title":"A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification","volume":"97","author":"Marinho","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.bspc.2016.07.010","article-title":"Heartbeat classification using projected and dynamic features of ECG signal","volume":"31","author":"Chen","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.patrec.2015.11.018","article-title":"Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals","volume":"70","author":"Azarnia","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/1475-925X-13-90","article-title":"A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals","volume":"13","author":"Huang","year":"2014","journal-title":"BioMed. Eng. Online"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102091","DOI":"10.1016\/j.bspc.2020.102091","article-title":"CraftNet: A deep learning ensemble to diagnose cardiovascular diseases","volume":"62","author":"Li","year":"2020","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5080\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:43:36Z","timestamp":1760139816000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5080"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":34,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145080"],"URL":"https:\/\/doi.org\/10.3390\/s22145080","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,6]]}}}