Hybridizing β-hill climbing with wavelet transform for denoising ECG signals

This paper introduces βHCWT, a hybrid of the β-hill climbing metaheuristic algorithm and wavelet transform (WT), as a new method for denoising electrocardiogram (ECG) signals. ECG signals are non-stationary signals that provide a graphical measure of electrical activities in human heart muscles. However, given their non-stationarity, these signals frequently encounter noise and a low signal-to-noise ratio (SNR). The selection of wavelet parameters is a challenging task that is usually performed based on empirical evidence or experience. Therefore, in this paper, β-hill climbing is applied to find the optimal wavelet parameters that can obtain the minimum mean square error (MSE) between the original and denoised ECG signals. The proposed method was tested on a standard ECG dataset from MIT-BIH while its performance was evaluated by using percentage root mean square difference (PRD) and SNR as criteria. Meanwhile, the effect of β-hill climbing on the performance of WT was tested by comparing the proposed method with WT. The proposed method was then compared with the genetic algorithm in consideration of the performance of the WT parameters and adaptive thresholding methods. The proposed method demonstrated an outstanding performance in removing noise from non-stationary signals, and the quality of the output signal was deemed favorable for medical diagnosis.