Multiperceptive Region of Spatial–Temporal Graph Convolutional Shrinkage Network for Arrhythmia Recognition-Reference-Cited by-同舟云学术

Multiperceptive Region of Spatial–Temporal Graph Convolutional Shrinkage Network for Arrhythmia Recognition

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Chen Yongtao¹^ORCID,Qiu Sen¹^ORCID,Wang Zhelong¹^ORCID,Zhao Hongyu¹^ORCID,Cao Xiaoyu²^ORCID

Affiliation:

1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education and the School of Control Science and Engineering, Dalian University of Technology, Dalian, China

2. School of Automation Science and Engineering and the MOE Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province, China

Xingliao Talent Project

Fundamental Research Funds for the Central Universities, China

Kweichow Moutai Group

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10466627.pdf?arnumber=10466627

Reference49 articles.

1. Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network

2. Classification of ECG Arrhythmia with Machine Learning Techniques

3. An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification

4. HA-ResNet: Residual Neural Network With Hidden Attention for ECG Arrhythmia Detection Using Two-Dimensional Signal

5. ECG Signals Segmentation Using Deep Spatiotemporal Feature Fusion U-Net for QRS Complexes and R-Peak Detection