ECG Heartbeat Classification Detection Based on WaveNet-LSTM

被引:0
作者
Qu, Yuanyuan [1 ]
Zhang, Nina [2 ]
Meng, Yue [1 ]
Qin, Zhiliang [1 ]
Lu, Qidong [1 ]
Liu, Xiaowei [1 ]
机构
[1] Weihai Beiyang Elect Grp Co Ltd, Weihai, Shandong, Peoples R China
[2] Weihai Beiyang Opt Elect Info Tech Co Ltd, Weihai, Shandong, Peoples R China
来源
2020 IEEE THE 4TH INTERNATIONAL CONFERENCE ON FRONTIERS OF SENSORS TECHNOLOGIES (ICFST 2020) | 2020年
关键词
ECG analysis; heartbeat classification; deep learning; WaveNet model; confusion matrix; NEURAL-NETWORK; MODEL;
D O I
10.1109/icfst51577.2020.9294765
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Electrocardiogram (ECG) can effectively record the potential difference of the body surface generated during the physiological function of the heart, and analyze and accurately discriminate the potential difference signal generated by the electrocardiogram, which can effectively prevent sudden diseases and reduce suddenness. ECG heartbeat is a powerful tool to diagnose several abnormal arrhythmias. Although the classification methods of arrhythmia improved significantly, but when detecting different types of cardiac abnormalities, especially when dealing with unbalanced data sets, it still does not provide acceptable performance. In this paper, we propose a method of data processing combined with the deep WaveNet-LSTM convolution model to solve this limitation of the current classification method. Furthermore, to prove the effectiveness of proposed method, using the MIT-Bill arrhythmia database, which considers intra- and inter-patient paradigms, and the AAMI EC57 standard. The evaluation results show that our method has achieved very good performance, and its accuracy of ECG abnormal signal detection reaches 96.8%.
引用
收藏
页码:54 / 58
页数:5
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