Denoising Ambulatory Electrocardiogram Signal Using Interval Dedependent Thresholds based Stationary Wavelet Transform

Abstract

Noise contamination in electrocardiogram (ECG) monitoring systems can lead to errors in analysis and diagnosis, resulting in a high false alarm rate (FAR). Various studies have been conducted to reduce or eliminate noise in ECG signals. However, some noise characteristics overlap with the frequency range of ECG signals, which occur randomly and are transient. This results in shape alteration and amplitude reduction in P and R waves. The author proposed a framework for eliminating noise in ECG signals using the stationary wavelet transform method and interval-dependent thresholds (IDT) based on the change point detection method to address these challenges. The proposed framework decomposes the input electrocardiogram (ECG) signal at a specific level using the Stationary Wavelet Transform method, resulting in detail and approximation coefficients. Interval detection focuses on the initial detailed coefficient, d1, chosen due to its significant content of noise coefficients, especially high-frequency noise. Subsequently, threshold values are computed for each interval. Hard and soft thresholding processes are then applied individually to each interval. Finally, reconstruction occurs using the inverse stationary wavelet transform method on the threshold coefficient outcomes. Two measurement matrices, root mean square error (RMSE) and percentage root mean squared difference (PRD), were used to measure the performance of the proposed framework. In addition, the proposed framework was compared to stationary wavelet transform (SWT) and discrete wavelet transform (DWT). The test results showed that the proposed method outperforms DWT and SWT. The proposed framework obtained an average increase in RMSE scores of 18% and 45% compared to the SWT and DWT methods, respectively, and PRD values of 17% and 37% compared to the SWT and DWT methods, respectively. So, using IDT in the stationary wavelet transform method can improve the denoising performance. With the development of this new framework for denoising ECG signals, we hope it can become an alternative method for other researchers to utilize in denoising ECG signals.