Stationary wavelet transform based ECG signal denoising method

Area of Science:

• Biomedical Engineering
• Signal Processing
• Cardiology

Background:

• Electrocardiogram (ECG) signals are vital for diagnosing cardiovascular diseases.
• ECG signals are susceptible to various noise types, including power line interference, baseline wandering, motion artifacts, and electromyogram noise.
• The non-stationary nature of ECG signals complicates noise removal.

Purpose of the Study:

• To propose and evaluate a novel denoising technique for ECG signals using stationary wavelet transform.
• To compare the performance of the proposed technique against other established denoising methods.
• To assess the effectiveness of noise reduction and preservation of ECG signal components.

Main Methods:

• A stationary wavelet transform-based denoising technique was developed.
• Comparative analysis included lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, and discrete wavelet transform.
• Performance was quantitatively evaluated using signal-to-noise ratio (SNR), percentage root-mean-square difference (PRD), and root mean square error (RMSE).

Main Results:

• The proposed stationary wavelet transform technique demonstrated superior performance in denoising ECG signals.
• The stationary wavelet transform method preserved more essential ECG signal components compared to other algorithms.
• Quantitative metrics confirmed the effectiveness of the proposed technique over conventional methods.

Conclusions:

• Stationary wavelet transform is a highly effective method for denoising ECG signals.
• The proposed technique offers an improved approach for noise reduction in ECG signal acquisition.
• This advancement can lead to more accurate diagnosis of cardiovascular diseases through cleaner ECG data.