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Article

Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch

1
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
2
Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(19), 5683; https://doi.org/10.3390/s20195683
Received: 26 August 2020 / Revised: 19 September 2020 / Accepted: 30 September 2020 / Published: 5 October 2020
(This article belongs to the Special Issue Smart Wearables for Cardiac Monitoring)
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 Poincaré 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. View Full-Text
Keywords: premature atrial contraction detection; Poincaré plot; premature ventricular contraction detection premature atrial contraction detection; Poincaré plot; premature ventricular contraction detection
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MDPI and ACS Style

Han, D.; Bashar, S.K.; Mohagheghian, F.; Ding, E.; Whitcomb, C.; McManus, D.D.; Chon, K.H. Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch. Sensors 2020, 20, 5683. https://doi.org/10.3390/s20195683

AMA Style

Han D, Bashar SK, Mohagheghian F, Ding E, Whitcomb C, McManus DD, Chon KH. Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch. Sensors. 2020; 20(19):5683. https://doi.org/10.3390/s20195683

Chicago/Turabian Style

Han, Dong, Syed K. Bashar, Fahimeh Mohagheghian, Eric Ding, Cody Whitcomb, David D. McManus, and Ki H. Chon. 2020. "Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch" Sensors 20, no. 19: 5683. https://doi.org/10.3390/s20195683

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