ECG signal denoising based on wavelet threshold algorithm

Abstract

This article discusses the application of wavelet threshold algorithms in signal denoising, which is crucial in removing noise interference in signal processing. The basics of wavelet theory and statistical characteristics of non-Gaussian noise are introduced, and existing threshold equations are improved and perfected for non-Gaussian noise backgrounds. The improved algorithm is then applied to the denoising of weak life signals, specifically electrocardiogram signals, and the results are analyzed. The combination of boosted wavelet and threshold functions is used for improvement, and the adaptive threshold of the Brige-Massart strategy is applied to process the electrocardiogram signal. Finally, the boosted wavelet threshold algorithm is found to be suitable for denoising complex Gaussian and non-Gaussian noise and can achieve good denoising effects.