ECG signal denoising using β-hill climbing algorithm and wavelet transform

Electrocardiogram (ECG) is a graphical recording of the electrical activity of human heart muscles. ECG is classified as a non-stationary signal. A major problem encountered with non-stationary signals is noise removal, particularly when the signal has a low signal-to-noise ratio (SNR). In this paper, the authors propose a hybrid method of β-hill climbing combined with wavelet transform for denoising ECG signals. Selecting wavelet parameters is a challenging task that is usually performed based on empirical evidence or experience. Therefore, β-hill climbing must find the optimal wavelet parameters for ECG signal denoising that can obtain the minimum mean square error between the original and the denoised ECG signals. The proposed method was tested using a standard ECG dataset established by MIT-BIH. The proposed hybrid method was also evaluated using two criteria, namely, percentage root mean square difference and SNR. The proposed method demonstrated outstanding noise reduction performance for ECG signals, and the quality of the denoised signal is suitable for clinical diagnosis.