The ability of an intelligent system to correctly classify and recognize patterns makes them particularly suitable for use in an expert system that aids in the interpretation of ECG signals. The features extracted from ECG are highly useful in diagnosis.Wavelet based methods present a best performance as irregularity measures and makes them suitable for ECG data analysis. In this paper, we propose an algorithm for detection of myocardial Ischemic episodes from Electrocardiogram (ECG) signal using Daubechies Wavelet transform technique.The ECG signal was denoised by removing the corresponding wavelet coefficients at higher scale. Analysis is carried out using MATLAB software. The algorithm was evaluated using two cases of data, the first case is with healthy subjects, and second case is with subjects affected by myocardial Ischemia. ECGs are obtained from MIT-BIH Arrhythmia Database which is manually annotated and developed for validation. From the results, it is concluded that Daubechies wavelets are best suitable for small datasets and are able to clearly demark the healthy and disease subjects such as myocardial ischemia subjects.
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.