sensors Article Premature Atrial and Ventricular Contraction Detection Using Photoplethysmographic Data from a Smartwatch Dong Han 1 , Syed Khairul Bashar 1 , Fahimeh Mohagheghian 1 , Eric Ding 2 , Cody Whitcomb 2 , David D. McManus 2 and Ki H. Chon 1,* 1 Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA;
[email protected] (D.H.);
[email protected] (S.K.B.);
[email protected] (F.M.) 2 Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA;
[email protected] (E.D.);
[email protected] (C.W.);
[email protected] (D.D.M.) * Correspondence:
[email protected] Received: 26 August 2020; Accepted: 30 September 2020; Published: 5 October 2020 Abstract: 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.