A LSTM and CNN Based Assemble Neural Network Framework for Arrhythmias Classification

Fan Liu, Xingshe Zhou, Jinli Cao, Zhu Wang, Hua Wang, Yanchun Zhang

科研成果: 书/报告/会议事项章节会议稿件同行评审

23 引用 (Scopus)

摘要

This paper puts forward a LSTM and CNN based assemble neural network framework to distinguish different types of arrhythmias by integrating stacked bidirectional long shot-term memory (SB-LSTM) network and two-dimensional convolutional neural network (TD-CNN). Particularly, SB-LSTM is used to mine the long-term dependencies contained in electrocardiogram (ECG) from two directions to model the overall variation trends of ECG, while TD-CNN aims at extracting local information of ECG to characterize the local features of ECG. Moreover, we design an ensemble empirical mode decomposition (EEMD) based signal decomposition layer and a support vector machine based intermediate result fusion layer, by which ECG can be analyzed more effectively, and the final classification results can be more accurate and robust. Experimental results on public INCART arrhythmia database show that our model surpasses three state-of-the-art methods, and obtains 99.1% of accuracy, 99.3% of sensitivity and 98.5% of specificity.

源语言英语
主期刊名2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
1303-1307
页数5
ISBN(电子版)9781479981311
DOI
出版状态已出版 - 5月 2019
活动44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, 英国
期限: 12 5月 201917 5月 2019

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2019-May
ISSN(印刷版)1520-6149

会议

会议44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
国家/地区英国
Brighton
时期12/05/1917/05/19

指纹

探究 'A LSTM and CNN Based Assemble Neural Network Framework for Arrhythmias Classification' 的科研主题。它们共同构成独一无二的指纹。

引用此