Spatiotemporal 3-D Variations Modeling With Self-Attention for Multilabel ECG Classification-Reference-Cited by-同舟云学术

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Spatiotemporal 3-D Variations Modeling With Self-Attention for Multilabel ECG Classification

Published:2024-06-01 Issue:11 Volume:24 Page:18710-18724
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Xia Pan¹^ORCID,Zhang Hao¹^ORCID,Yao Yicheng¹^ORCID,Xu Lirui¹^ORCID,Bai Zhongrui¹^ORCID,Chen Xianxiang¹,Li Zhenfeng¹,Zhu Yusi²^ORCID,Li Xiaoran³,Du Lidong¹^ORCID,Wang Peng¹,Fang Zhen¹^ORCID

Affiliation:

1. State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

2. School of Physics and Electronic Information, Yunnan Normal University, Kunming, China

3. Beijing Friendship Hospital, Capital Medical University, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Scientific Research Foundation Project Funded by Education Department of Yunnan Province

Yunnan Fundamental Research Projects

Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10546898/10509628.pdf?arnumber=10509628

Reference32 articles.

1. Detection and Localization of Myocardial Infarction using K-nearest Neighbor Classifier

2. Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals

3. Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction

4. High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal

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