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

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 calcul...

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Bibliographic Details
Published in:IEEE access 2017, Vol.5, p.11437-11443
Main Authors: Keonsoo Lee, Hyung Oh Choi, Se Dong Min, Jinseok Lee, Gupta, Brij B., Yunyoung Nam
Format: Article
Language:English
Subjects:
Arrhythmia
Artificial neural networks
Atrial fibrillation
Cameras
Cardiac arrhythmia
Electrocardiography
Entropy (Information theory)
Fibrillation
Ground truth
Heart beat
machine learning
Neural networks
photoplethysmography
smartphone
Smartphones
Support vector machines
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2700488