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About Application of Deep Convolutional Neural Network For Automated Detection of Myocardial Infarction Using ECG Signals
This document summarizes a research paper that proposes applying a convolutional neural network (CNN) algorithm to automatically detect myocardial infarction (MI) using electrocardiogram (ECG) signals. The authors trained an 11-layer CNN on ECG data from 200 subjects, achieving 93.53% accuracy at detecting normal vs. MI heartbeats with noise, and 95.22% accuracy without noise removal. This CNN approach eliminates the need for manual feature extraction and can accurately analyze unknown ECG signals, even with noise. The system may help clinicians diagnose MI from ECG data.
Detailed Information
| Author: | ['Alex Loeza'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 19 |
| Language: | English |
| Format: | |
| Price: | FREE |
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