A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphoneopen access
- Authors
- Lee, Keonsoo; Choi, Hyung Oh; Min, Se Dong; Lee, Jinseok; Guptha, Brij B.; Nam, Yunyoung
- Issue Date
- 2017
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Arrhythmia; atrial fibrillation; machine learning; photoplethysmography; smartphone
- Citation
- IEEE Access, v.5, pp 11437 - 11443
- Pages
- 7
- Journal Title
- IEEE Access
- Volume
- 5
- Start Page
- 11437
- End Page
- 11443
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/8437
- DOI
- 10.1109/ACCESS.2017.2700488
- ISSN
- 2169-3536
- 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.
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- Appears in
Collections - College of Engineering > Department of Computer Science and Engineering > 1. Journal Articles
- College of Medical Sciences > Department of Medical IT Engineering > 1. Journal Articles
- College of Medicine > Department of Internal Medicine > 1. Journal Articles
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