Computational Diagnostic Techniques for Electrocardiogram Signal Analysis.
Sensors (Basel)
; 20(21)2020 Nov 05.
Article
in En
| MEDLINE
| ID: mdl-33167558
ABSTRACT
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Signal Processing, Computer-Assisted
/
Cardiovascular Diseases
/
Electrocardiography
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limits:
Humans
Language:
En
Journal:
Sensors (Basel)
Year:
2020
Type:
Article
Affiliation country:
China