ECG classification based on transfer learning and deep convolution neural network(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2018年第11期
- Page:
- 1307-1312
- Research Field:
- 医学信号处理与医学仪器
- Publishing date:
Info
- Title:
- ECG classification based on transfer learning and deep convolution neural network
- Author(s):
- ZHA Xuefan1; YANG Feng1; 2; WU Yu’nan1; LIU Ying1; YUAN Shaofeng1; 2
- 1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; 2. Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
- Keywords:
- ; Keywords: electrocardiogram heartbeat classification; transfer learning; deep learning; two-dimensional deep convolution neural network; one-dimensional deep convolution neural network; ImageNet dataset
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2018.11.013
- Abstract:
- Abstract: One-dimensional deep convolution neural networks (1D-DCNN) for electrocardiogram (ECG) classification shows limitations in identifying various diseases and extracting the best morphological features. Herein, a method combining transfer learning and two-dimensional deep convolution neural network (2D-DCNN), AlexNet, is proposed to identify ECG images directly. Firstly, the ECG signals within the 75 ms before and after R wave were intercepted, and one-dimensional ECG voltage signals were converted into two-dimensional grayscale image signals. Then, a 2D-DCNN based on AlexNet was established to classify the ECG heartbeat samples. The weights were initialized by parameters which were pre-trained on Alexnet using a large-scale dataset ImageNet. The proposed method achieved an accuracy of 98% on the MIT-BIH arrhythmia database, and maintained a high accuracy at different signal-to-noise ratios, which verified the good robustness of the proposed method in ECG classification. The proposed method was also compared with 1D-DCNN using different activation functions and other deep learning methods with favorable performances to evaluate the performance of 2D-DCNN. The quantitative results demonstrated that compared with the optimal 1D-DCNN, the proposed method combining transfer learning with 2D-DCNN improves the accuracy rate, sensitivity and specificity by 2%, 0.6% and 4%, respectively, and that the proposed algorithm is better than other existing algorithms in both binary/multi-class classification tasks.
Last Update: 2018-11-22