In recent years, cardiovascular diseases have become common. Serious health problems arise in the human body as a result of an unhealthy lifestyle, the use of alcohol and tobacco, obesity, stress, and dietary changes. This has complicated surgeons' ability to diagnose heart failure at the right time. A heart attack occurs when the blood flow that brings oxygen to the heart muscle is severely condensed or cut off completely. ECG is a medical test that is used in the detection of heart attacks in patients. Extracting the essential features from ECG images is the most crucial task. The key features are extracted using connected component analysis, hierarchical centroid, Hough line transform, and height and width. Various techniques like Fast Fourier Transform, Discrete Fourier Transform, Decision Tree and Principal Component Analysis are used to predict heart failure. In this model, we are going to examine ECG signal images and detect whether the person is prone to heart attack or not. A comparative study of different models showed that the proposed work enhanced the previous accuracy score in predicting heart failure using FFT.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.