|
Analysis of 12-lead electrocardiogram signal based on deep learning
Yangxin Chen1, Gang Du2, Jiangting Mai1, Wenhao Liu1, Xiaoqiao Wang3, Junxia You4, Yuyang Chen5, Yong Xie1, Hai Hu6, 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 Bioinformatics, Guangzhou Gencoding Lab, Guangzhou, Guangdong Province, China
5 Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, China
6 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
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/IJHR.IJHR_4_18
|
|
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. |
