Clinical & Experimental Cardiology
Open Access

Abstract

Exact Wavelet Threshold Determined by the ACF of Denoised ECG Signals

Wen Chen, Kaimin Yu

The heartbeat signal monitored by wearable sensors can detect cardiovascular diseases in real time, which is one of the deadliest diseases in the world. However, it is contaminated by noise, seriously affecting the accuracy of diagnosis. This article proposes a real-time and accurate wavelet thresholding method that utilizes normalized autocorrelation functions to accurately estimate electrocardiogram signals, i.e., accurately estimate noise. Compared to traditional wavelet thresholding methods, it can effectively remove various types of noise, especially actual noise. The method can adaptively process complex noise without parameter adjustment, overcoming the shortcomings of existing methods that cannot accurately obtain wavelet thresholds in real time. The method can optimize the parameters of other filters besides various wavelet transforms to enhance the SNR of periodic signals more effectively

Published Date: 2025-02-20;