Paper Info
Title
Analyzing electrocardiogram signals obtained from a nymi band to detect atrial fibrillation
Abstract
In this paper, we propose a method for detecting atrial fibrillation (AF) from electrocardiogram (ECG) signals obtained from a wearable device. The proposed method uses three classification methods: neural networks (NNs), k-nearest neighbors (kNN), and decision trees (DT). The results from each of the three classifiers are combined using a voting system to make the final decision as to whether AF is present. To develop the classification system, we collected data from 61 subjects using a Nymi Band that is wrist-worn ECG monitoring device. From these signals, we extracted the root-mean square of the successive differences (RMSSD) and the Shannon entropy (ShE) of the RR interval, QS interval, and R peak amplitude. These properties were then used as features to train the classifiers. The accuracy, sensitivity, specificity, and precision of this classifier were 97.94%, 100.00%, 96.72%, and 94.74%, respectively for dataset with six features. The ensemble method of NNs, kNN, and DT was evaluated. Depending on the rules for ensemble, the accuracy, sensitivity, specificity, and precision are different among those classifiers. With a rule of unanimous determination for AF, false positive is decreased and false negative is increased. With a rule of unanimous determination for NSR, false positive is increased and false negative decreased. Even though accuracies of each classifier are depending on the set of features, with ensemble method, the accuracy of AF detection can be preserved.
Year
DOI
Venue
2020
10.1007/s11042-018-7075-1
Multimedia Tools and Applications
Keywords
DocType
Volume
Arrhythmia, Atrial fibrillation, Smartphone, Electrocardiogram
Journal
79
Issue
ISSN
Citations 
23-24
1573-7721
0
PageRank 
References 
Authors
0.34
3
5
Name
Order
Citations
PageRank
Keonsoo Lee100.34
Sora Kim200.34
Hyung Oh Choi300.34
Jinseok Lee430341.82
Yunyoung Nam526639.60