A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction.
10.7507/1001-5515.202212010
- Author:
Chuang HAN
1
;
Wenge QUE
2
;
Zhizhong WANG
3
;
Songwei WANG
3
;
Yanting LI
1
;
Li SHI
2
Author Information
1. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, P. R. China.
2. Department of Automation, Tsinghua university, Beijing 100000, P. R. China.
3. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450000, P. R. China.
- Publication Type:Journal Article
- Keywords:
Electrocardiogram;
Feature extraction;
Intelligent auxiliary diagnosis;
Machine learning;
Myocardial infarction
- MeSH:
Humans;
Electrocardiography;
Myocardial Infarction/diagnosis*;
Recognition, Psychology
- From:
Journal of Biomedical Engineering
2023;40(5):1019-1026
- CountryChina
- Language:Chinese
-
Abstract:
Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.
