International Journal of Heart Rhythm

ORIGINAL ARTICLE
Year
: 2018  |  Volume : 3  |  Issue : 2  |  Page : 55--59

Analysis of 12-lead electrocardiogram signal based on deep learning


Yangxin Chen1, Gang Du2, Jiangting Mai1, Wenhao Liu1, Xiaoqiao Wang3, Junxia You5, Yuyang Chen4, Yong Xie1, Hai Hu5, Shuxian Zhou1, Jingfeng Wang1 
1 Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University; Guangdong Province Key Laboratory of Arrhythmia and Electrophysiology, Guangzhou, Guangdong Province, China
2 Zhujiang Hospital, Southern Medical University; Department of Bioinformatics, Guangzhou Gencoding Lab, Guangzhou, Guangdong Province, China
3 Department of Anesthesiology, Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
4 Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
5 Department of Tumor Chemotherapy, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China

Correspondence Address:
Prof. Jingfeng Wang
Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, Guangdong Province
China

Background: In this work, a deep learning method is proposed to identify the types of arrhythmia. Methods: The 12-lead electrocardiogram signal is first denoised by filters to eliminate the baseline drift and the myoelectric interference. Then, the filtered signal is sliced into beats and sent to a deep neural network, which contains four convolutional layers, two gated recurrent unit layers, and one full-connected layer. Features in both the spatial domain and the time-frequency domain can be extracted implicitly by the deep neural network, instead of being extracted manually. Results: On the test split of the dataset, our neural network model achieves an accuracy of 98.15%. Among the accuracies for the four types of arrhythmia, respectively, the lowest one is 96% and the highest is 99%. Our model is must better than a baseline support vector machines classifier, with a test accuracy of 73.54%. Conclusion: The results give a supportive evidence to make our model clinically applicable to assist physicians in diagnosing certain diseases.


How to cite this article:
Chen Y, Du G, Mai J, Liu W, Wang X, You J, Chen Y, Xie Y, Hu H, Zhou S, Wang J. Analysis of 12-lead electrocardiogram signal based on deep learning.Int J Heart Rhythm 2018;3:55-59


How to cite this URL:
Chen Y, Du G, Mai J, Liu W, Wang X, You J, Chen Y, Xie Y, Hu H, Zhou S, Wang J. Analysis of 12-lead electrocardiogram signal based on deep learning. Int J Heart Rhythm [serial online] 2018 [cited 2019 Feb 17 ];3:55-59
Available from: http://www.ijhronline.org/article.asp?issn=2352-4197;year=2018;volume=3;issue=2;spage=55;epage=59;aulast=Chen;type=0