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Atrial fibrillation classification based on convolutional neural networks

Authors
Lee, Kwang-SigJung, SunghoonGil, YeongjoonSon, Ho Sung
Issue Date
29-10월-2019
Publisher
BMC
Keywords
Atrial fibrillation; Convolutional neural networks; Alex networks; Residual networks
Citation
BMC MEDICAL INFORMATICS AND DECISION MAKING, v.19, no.1
Indexed
SCIE
SCOPUS
Journal Title
BMC MEDICAL INFORMATICS AND DECISION MAKING
Volume
19
Number
1
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/62162
DOI
10.1186/s12911-019-0946-1
ISSN
1472-6947
Abstract
Background: The global age-adjusted mortality rate related to atrial fibrillation (AF) registered a rapid growth in the last four decades, i.e., from 0.8 to 1.6 and 0.9 to 1.7 per 100,000 for men and women during 1990-2010, respectively. In this context, this study uses convolutional neural networks for classifying (diagnosing) AF, employing electrocardiogram data in a general hospital. Methods: Data came from Anam Hospital in Seoul, Korea, with 20,000 unique patients (10,000 normal sinus rhythm and 10,000 AF). 30 convolutional neural networks were applied and compared for the diagnosis of the normal sinus rhythm vs. AF condition: 6 Alex networks with 5 convolutional layers, 3 fully connected layers and the number of kernels changing from 3 to 256; and 24 residual networks with the number of residuals blocks (or kernels) varying from 8 to 2 (or 64 to 2). Results: In terms of the accuracy, the best Alex network was one with 24 initial kernels (i.e., kernels in the first layer), 5, 268,818 parameters and the training time of 89 s (0.997), while the best residual network was one with 6 residual blocks, 32 initial kernels, 248,418 parameters and the training time of 253 s (0.999). In general, the performance of the residual network improved as the number of its residual blocks (its depth) increased. Conclusion: For AF diagnosis, the residual network might be a good model with higher accuracy and fewer parameters than its Alex-network counterparts.
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