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A Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphoneopen access

Authors
Lee, KeonsooChoi, Hyung OhMin, Se DongLee, JinseokGuptha, 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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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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