{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:33:07Z","timestamp":1776889987991,"version":"3.51.2"},"reference-count":42,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,10,5]],"date-time":"2020-10-05T00:00:00Z","timestamp":1601856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["1R01 HL137734"],"award-info":[{"award-number":["1R01 HL137734"]}]},{"DOI":"10.13039\/501100000930","name":"NSF","doi-asserted-by":"publisher","award":["1522087"],"award-info":[{"award-number":["1522087"]}],"id":[{"id":"10.13039\/501100000930","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC\/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC\/PVC detection results of the Poincar\u00e9 plot method. The new PAC\/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC\/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC\/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC\/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC\/PVC.<\/jats:p>","DOI":"10.3390\/s20195683","type":"journal-article","created":{"date-parts":[[2020,10,5]],"date-time":"2020-10-05T10:43:48Z","timestamp":1601894628000},"page":"5683","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-7371","authenticated-orcid":false,"given":"Dong","family":"Han","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0180-1980","authenticated-orcid":false,"given":"Syed Khairul","family":"Bashar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6653-7148","authenticated-orcid":false,"given":"Fahimeh","family":"Mohagheghian","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6904-8534","authenticated-orcid":false,"given":"Eric","family":"Ding","sequence":"additional","affiliation":[{"name":"Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3786-2072","authenticated-orcid":false,"given":"Cody","family":"Whitcomb","sequence":"additional","affiliation":[{"name":"Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9343-6203","authenticated-orcid":false,"given":"David D.","family":"McManus","sequence":"additional","affiliation":[{"name":"Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4422-4837","authenticated-orcid":false,"given":"Ki H.","family":"Chon","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1109\/TBCAS.2015.2477437","article-title":"Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions","volume":"9","author":"Marozas","year":"2015","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_2","first-page":"815","article-title":"Arrhythmia discrimination using a smart phone","volume":"19","author":"Chong","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1016\/j.jacc.2018.03.003","article-title":"Smartwatch Algorithm for Automated Detection of Atrial Fibrillation","volume":"71","author":"Bumgarner","year":"2018","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sanna, T. (2018). Long-term monitoring to detect atrial fibrillation with the indwelling implantable cardiac monitors. Int. J. Stroke, 1747493018790023.","DOI":"10.1177\/1747493018790023"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1007\/s10439-009-9740-z","article-title":"Automatic Real Time Detection of Atrial Fibrillation","volume":"37","author":"Dash","year":"2009","journal-title":"Ann. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1001\/jamacardio.2018.0136","article-title":"Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch","volume":"3","author":"Tison","year":"2018","journal-title":"JAMA Cardiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Oh, S.L., Ng, E.Y.K., Tan, R.S., and Acharya, U.R. (2018). Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput. Biol. Med.","DOI":"10.1016\/j.compbiomed.2018.06.002"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1007\/s40846-017-0294-5","article-title":"A Diagnostic System for Detection of Atrial and Ventricular Arrhythmia Episodes from Electrocardiogram","volume":"38","author":"Chetan","year":"2018","journal-title":"J. Med. Biol. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1109\/TBME.2007.903707","article-title":"A detector for a chronic implantable atrial tachyarrhythmia monitor","volume":"55","author":"Sarkar","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hayano, J., Kisohara, M., Masuda, Y., and Yuda, E. (2019, January 11\u201314). Detection of paroxysmal atrial fibrillation by Lorenz plot imaging of ECG R-R intervals. Proceedings of the International Forum on Medical Imaging in Asia 2019, Vancouver, BC, Canada.","DOI":"10.1117\/12.2523310"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5787582","DOI":"10.1155\/2019\/5787582","article-title":"Intelligent Analysis of Premature Ventricular Contraction Based on Features and Random Forest","volume":"2019","author":"Xie","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3149","DOI":"10.1109\/TBME.2013.2270083","article-title":"Heart Rate Turbulence Analysis Based on Photoplethysmography","volume":"60","author":"Gil","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"025003","DOI":"10.1088\/1361-6579\/ab029c","article-title":"Detection of atrial fibrillation using a wrist-worn device","volume":"40","author":"Marozas","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11517-018-1886-0","article-title":"Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?","volume":"57","author":"Fallet","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e12770","DOI":"10.2196\/12770","article-title":"Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study","volume":"7","author":"Kwon","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_17","first-page":"1","article-title":"Photoplethysmography based atrial fibrillation detection: A review","volume":"3","author":"Pereira","year":"2020","journal-title":"NPG Digit. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1109\/JBHI.2019.2950574","article-title":"Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data","volume":"24","author":"Eerikainen","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.hroo.2020.02.002","article-title":"Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application","volume":"1","author":"Aschbacher","year":"2020","journal-title":"Heart Rhythm. O2"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Haddad, S., Harju, J., Tarniceriu, A., Halkola, T., Parak, J., Korhonen, I., Yli-Hankala, A., and Vehkaoja, A. (2019, January 26\u201328). Ectopic Beat Detection from Wrist Optical Signals for Sinus Rhythm and Atrial Fibrillation Subjects. Proceedings of the XV Mediterranean Conference on Medical and Biological Engineering and Computing\u2014MEDICON 2019, Coimbra, Portugal.","DOI":"10.1007\/978-3-030-31635-8_18"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, Y., Wang, H., Zhang, H., Chen, Y., and Lip, G.Y.H. (2020). Population-Based Screening or Targeted Screening Based on Initial Clinical Risk Assessment for Atrial Fibrillation: A Report from the Huawei Heart Study. J. Clin. Med., 9.","DOI":"10.3390\/jcm9051493"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e16443","DOI":"10.2196\/16443","article-title":"Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study","volume":"22","author":"Kwon","year":"2020","journal-title":"J. Med Internet Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shashikumar, S.P., Shah, A.J., Li, Q., Clifford, G.D., and Nemati, S. (2017, January 16\u201319). A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology. Proceedings of the 2017 IEEE EMBS International Conference on Biomedical Health Informatics (BHI), Orlando, FL, USA.","DOI":"10.1109\/BHI.2017.7897225"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shashikumar, S.P., Shah, A.J., Clifford, G.D., and Nemati, S. (2018, January 19\u201323). Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219912"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"084001","DOI":"10.1088\/1361-6579\/aad2c0","article-title":"Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions","volume":"39","author":"Bonomi","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nemati, S., Ghassemi, M.M., Ambai, V., Isakadze, N., Levantsevych, O., Shah, A., and Clifford, G.D. (2016, January 16\u201320). Monitoring and detecting atrial fibrillation using wearable technology. Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591456"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tarniceriu, A., Harju, J., Yousefi, Z.R., Vehkaoja, A., Parak, J., Yli-Hankala, A., and Korhonen, I. (2018, January 18\u201321). The Accuracy of Atrial Fibrillation Detection from Wrist Photoplethysmography. A Study on Post-Operative Patients. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8513197"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shen, Y., Voisin, M., Aliamiri, A., Avati, A., Hannun, A., and Ng, A. (2019, January 4\u20138). Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330657"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aliamiri, A., and Shen, Y. (2018, January 4\u20137). Deep learning based atrial fibrillation detection using wearable photoplethysmography sensor. Proceedings of the IEEE EMBS International Conference on Biomedical Health Informatics (BHI), Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2018.8333463"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1088\/1361-6579\/aa5dd7","article-title":"Detection of atrial fibrillation episodes using a wristband device","volume":"38","author":"Corino","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lemay, M., Fallet, S., Renevey, P., Sol\u00e0, J., Leupi, C., Pruvot, E., and Vesin, J.M. (2016, January 11\u201314). Wrist-located optical device for atrial fibrillation screening: A clinical study on twenty patients. Proceedings of the 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada.","DOI":"10.22489\/CinC.2016.200-350"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e009351","DOI":"10.1161\/JAHA.118.009351","article-title":"Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo-Plethysmography at the Wrist","volume":"7","author":"Bonomi","year":"2018","journal-title":"J. Am. Heart Assoc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e13850","DOI":"10.2196\/13850","article-title":"Accuracy and Usability of a Novel Algorithm for Detection of Irregular Pulse Using a Smartwatch Among Older Adults: Observational Study","volume":"3","author":"Ding","year":"2019","journal-title":"JMIR Cardio"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/S0010-4809(84)80017-8","article-title":"The use of cross-correlation function for the alignment of ECG waveforms and rejection of extrasystoles","volume":"17","author":"Abboud","year":"1984","journal-title":"Comput. Biomed. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"128869","DOI":"10.1109\/ACCESS.2019.2939943","article-title":"An Accurate QRS Complex and P Wave Detection in ECG Signals Using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Approach","volume":"7","author":"Hossain","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Han, D., Bashar, S.K., Lazaro, J., Ding, E., Whitcomb, C., McManus, D.D., and Chon, K.H. (2019, January 23\u201327). Smartwatch PPG Peak Detection Method for Sinus Rhythm and Cardiac Arrhythmia. Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, German.","DOI":"10.1109\/EMBC.2019.8857325"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bashar, S., Hossain, M.B., Ding, E., Walkey, A., McManus, D., and Chon, K. (2020). Atrial Fibrillation Detection during Sepsis: Study on MIMIC III ICU Data*. IEEE J. Biomed. Health Inform., 1.","DOI":"10.1109\/JBHI.2020.2995139"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-49092-2","article-title":"Atrial Fibrillation Detection from Wrist Photoplethysmography Signals Using Smartwatches","volume":"9","author":"Bashar","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1111\/j.1445-5994.1995.tb00573.x","article-title":"Application of the Poincar\u00e9 plot to heart rate variability: A new measure of functional status in heart failure","volume":"25","author":"Kamen","year":"1995","journal-title":"Aust. N. Z. J. Med."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Salehizadeh, S.M.A., Dao, D., Bolkhovsky, J., Cho, C., Mendelson, Y., and Chon, K.H. (2016). 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"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5683\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:16:41Z","timestamp":1760177801000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5683"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,5]]},"references-count":42,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20195683"],"URL":"https:\/\/doi.org\/10.3390\/s20195683","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,5]]}}}