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Computer Aided Methods for Multiple Heart Disease Detection using ECG Signal: A Review

by Padmavathi C., Veenadevi S.V.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 45
Year of Publication: 2023
Authors: Padmavathi C., Veenadevi S.V.
10.5120/ijca2023922560

Padmavathi C., Veenadevi S.V. . Computer Aided Methods for Multiple Heart Disease Detection using ECG Signal: A Review. International Journal of Computer Applications. 184, 45 ( Feb 2023), 44-50. DOI=10.5120/ijca2023922560

@article{ 10.5120/ijca2023922560,
author = { Padmavathi C., Veenadevi S.V. },
title = { Computer Aided Methods for Multiple Heart Disease Detection using ECG Signal: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2023 },
volume = { 184 },
number = { 45 },
month = { Feb },
year = { 2023 },
issn = { 0975-8887 },
pages = { 44-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number45/32610-2023922560/ },
doi = { 10.5120/ijca2023922560 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:03.103461+05:30
%A Padmavathi C.
%A Veenadevi S.V.
%T Computer Aided Methods for Multiple Heart Disease Detection using ECG Signal: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 45
%P 44-50
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cardiology is a group of disease that affect the heart and its vessels. All heart conditions characterized by blockage of blood vessels, myocardial problems, valve malfunctions and varied heart rhythms are called heart abnormalities. The heart disease is one of the proven causes of death worldwide. Mortality resulting from heart disease can be reduced if the ailments are detected in an initial stage which helps in treating the patients on time. Cardiac abnormalities are reflected in the morphological features of the 12-lead clinical ECG signal. A lesser degree of detection time for doctors to analyze long-term electrocardiogram data and detect slight deviations in the electrocardiogram morphology. Automated computational diagnostic methods using deep learning techniques need to be developed to improve the performance of conventional machine learning based methods used for cardiac disease detection. The review article is therefore intended to present a detailed overview of the work on different computer aided automated methods used by many researchers from many years to automatically detect various heart ailments by characterizing and classifying ECG signal. This work includes a brief introduction on major heart ailments including CAD, MI, CHF, Cardiomyopathy, their typical ECG patterns and characteristics. The credibility of traditional computer aided methods used to detect multiple heart ailments is explored and further the deep learning techniques required to improve the performance of existing methods is discussed. From the results obtained by many researchers it is also revealed that the classification performance is to be improved by using deep learning techniques.

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Index Terms

Computer Science
Information Sciences

Keywords

Electrocardiogram (ECG) coronary artery disease (CAD) myocardial infarction (MI) congestive heart failure (CHF) cardiomyopathy deep learning (DL) machine learning(ML)

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