Name
Title | ||
|---|---|---|
Digital Image Processing Features Of Smartwatch Photoplethysmography For Cardiac Arrhythmia Detection |
Abstract | ||
|---|---|---|
The aim of our work is to design an algorithm to detect premature atrial contraction (PAC), premature ventricular contraction (PVC), and atrial fibrillation (AF) among normal sinus rhythm (NSR) using smartwatch photoplethysmographic (PPG) data. Novel image processing features and two machine learning methods are used to enhance the PAC/PVC detection results of the Poincare plot method. Compared with support vector machine (SVM) methods, the Random Forests (RF) method performs better. It yields a 10-fold cross validation (CV) averaged sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and accuracy for PAC/PVC labels of 63%, 98%, 83%, 94%, and 93%, respectively, and a 10-fold CV averaged sensitivity, specificity, PPV, NPV, and accuracy for AF subjects of 92%, 96%, 85%, 98%, and 95%, respectively. This is one of the first studies to derive image processing features from Poincare plots to further enhance the accuracy of PAC/PVC detection using PPG recordings from a smartwatch. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9176142 | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 |
DocType | Volume | ISSN |
Conference | 2020 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Han | 1 | 14 | 9.84 |
| S. K. Bashar | 2 | 24 | 5.25 |
| Fearass Zieneddin | 3 | 0 | 0.34 |
| Eric Ding | 4 | 0 | 0.34 |
| Cody Whitcomb | 5 | 0 | 0.34 |
| David D McManus | 6 | 104 | 14.62 |
| Ki H Chon | 7 | 367 | 74.38 |