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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Yunyoung Nam Keonsoo Lee Hyung Oh Choi Se Dong Min Jinseok Lee Brij B. Gupta |
| Abstract | This paper evaluated three methods of atrial fibrillation (AF) detection in Korean patients using 149 records of photoplethysmography signals from 148 participants: the k-nearest neighbor (kNN), neural network (NN), and support vector machine (SVM) methods. The 149 records are preprocessed to calculate the root-mean square of the successive differences in the R-R intervals and Shannon entropy which are validated from x-means and Massachusetts Institute of Technology and Beth Israel Hospital database for the features for AF detection. A smartphone camera was used to obtain photoplethysmography signals. Clinicians labeled 29 records by referring to the electrocardiogram signals. These labeled records were used as a ground truth set to evaluate the accuracy of each method. In the experiments, the kNN, NN, and SVM methods achieved 98.65%, 99.32%, and 97.98% accuracies, respectively. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2017.2700488 |
| Journal | IEEE Access |
| Volume Number | 5 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2017-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Arrhythmia Atrial Fibrillation Machine Learning Photoplethysmography Smartphone |
| Content Type | Text |
| Resource Type | Article |
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