Comparison on wavelet adaptive filter performance in denoising ECG signal

The acceleration of the development of electrocardiogram (ECG) signal denoising techniques has been observed in response to the pressing issue of heart failure. Given the potential fatality associated with heart failure, researchers have devised a practical approach to mitigate the influence of noise in the ECG data, thereby preventing erroneous diagnoses and unnecessary medical interventions. It is imperative to employ a precise denoising methodology to obtain an ECG signal that is devoid of noise. To ensure the accuracy and reliability of the ECG signal, it is necessary to eliminate significant sources of noise, including baseline wander (BW), powerline interference (PLI), motion artifact (MA), and electromyogram (EMG). The study suggests that the filtering stage should utilise filters that are based on adaptive filters (AF). The design of the AF and the performance comparison observed in the signal to noise ratio (SNR) test will be conducted utilising the MAT LAB simulation software. The findings indicate that combining decompose wavelet transform (DWT) and adaptive filter (AF) may improve performance compared to DWT and AF alone. The best combinations to reduce noise signals from ECG are 30.01 dB, 29.67 dB, 23.72 dB, and 28.31 dB for BW, PLI, EMG, and MA respectively.