Automatic Identifcation of Heart Block Precise Location Based on Sparse Connection Residual Network.
10.3969/j.issn.1671-7104.2019.02.003
- Author:
Ji QI
1
;
Ruiqing ZHANG
1
;
Yang SHEN
1
;
Shijie CHANG
1
;
Xiangzheng SHA
1
Author Information
1. Department of Biomedical Engineering, China Medical University, Shenyang, 110122.
- Publication Type:Journal Article
- Keywords:
CNN;
deep learning;
heart block
- MeSH:
Algorithms;
Arrhythmias, Cardiac;
diagnostic imaging;
Bundle-Branch Block;
diagnostic imaging;
Electrocardiography;
Humans;
Neural Networks (Computer)
- From:
Chinese Journal of Medical Instrumentation
2019;43(2):86-89
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To classify Right Bundle Branch Block (RBBB),Left Bundle Branch Block (LBBB) and normal ECG signals automatically.
METHODS:The MIT-BIH database was used as experimental data sources.The training set and test set were extracted for training and testing network models.Based on convolutional neural network,this paper proposed the core algorithm:sparse connection residual network.Compared the sparse connected residual network with classic network models,then evaluated the recognition effect of the model.
RESULTS:The accuracy of the test set the MIT-BIH database was 95.2%,the result is better than classic network models.
CONCLUSIONS:The algorithm proposed in this paper can assist doctors in the diagnosis of heart block related disease and place a high value on clinical application.
