Multi-Channel Lightweight Convolutional Neural Network for Remote Myocardial Infarction Monitoring

Yangjie Cao, Tingting Wei, Nan Lin, Di Zhang, Joel J.P.C. Rodrigues

研究成果: Conference contribution

抄録

Remote Myocardial Infarction (RMI) monitoring uses electronic devices to detect the electrocardiogram changes and inform the doctor in emergency conditions, which is an effective solution to save the patient's life. In this paper, we propose the Multi-Channel Lightweight CNN (MCL-CNN), which combines electrocardiogram signals from four leads (v2, v3, v5 and aVL) to detect the Anterior MI (AMI). Its multi-channel design allows the convolution of each lead to be independent of each other, and allowing them to find the filter that best suits them. In addition, constructing a lightweight network using different convolutional combinations in the MCL-CNN model, which makes the network has certain advantages in computing runtime parameters and more suitable for mobile devices. Meanwhile, we use balanced cross entropy to solve the problem of dataset class imbalance. These strategies make the MCL-CNN suitable for multi-lead ECG processing. Experimental results using public ECG datasets obtained from the PTB diagnostic database demonstrate that MCL-CNN's accuracy is 96.65%.

本文言語English
ホスト出版物のタイトル2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728151786
DOI
出版ステータスPublished - 2020 4
イベント2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Seoul, Korea, Republic of
継続期間: 2020 5 252020 5 28

出版物シリーズ

名前2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings

Conference

Conference2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020
CountryKorea, Republic of
CitySeoul
Period20/5/2520/5/28

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

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