Electrocardiogram (EKG or ECG) is an important electrical activity of the human Heart. ECG is used for the primary diagnosis of heart diseases since it shows the electrophysiology of the heart and the ischemic changes that may occur like the myocardial infarction, conduction defects, and arrhythmia. But, in real condition, ECG is often corrupted by different artifacts and noises. For the purpose of quality diagnosis, the ECG signal must be clearly de-noised to remove all noises and artifacts from the signal. In this paper, we present the Wavelet Transform, a new approach in digital signal processing to filter the ECG signal. Different ECG signals from M IT/BIH arrhythmia database are used with added 10dB, 5dB & 0dB Power Line Interference (PLI) noise which is common in ECG signal. The results were evaluated using M ATLAB software. Basically, two synthesis parameters M ean Square Error (M SE) and Signal to Noise ratio (SNR) have been used. The prime aim of this paper is to adapt the discrete wavelet transform (DWT) to improve the (ECG) signal quality for better clinical diagnosis. The evaluated results have been compared with Butterworth IIR filter. The proposed method shows improvement in output SNRo for 5dB noise is 98.5% and for 10dB noise is 95.7%.
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.