Enhanced ECG Record Quality: Integrated Artifact Suppression Using Soft Threshold on Wavelet Coefficients and Adaptive Filter Model

The Independent Component Analysis (ICA) method has been demonstrated as an effective tool for separating desired signals and artifacts in the processing of biomedical signals, particularly in electrocardiogram (ECG) recordings through blind source separation (BSS).Unwanted components, which propagate through the body to the electrodes and mix with the recorded signal, are analyzed into independent components (ICs).However, the unwanted ICs identified as artifacts may also contain valuable information, resulting in a loss of information if these ICs are removed entirely.To address this issue, a combined solution of wavelet decomposition and ICA is proposed.Wavelet decompositions are performed on the unwanted ICs, and the application of a threshold level to the wavelet coefficients minimizes the loss of information in the received signal.A proposed solution utilizing the wavelet-based ICA (wICA) algorithm effectively removes artifacts, reducing distortion in the amplitude and phase of the ECG signal.Consequently, the resulting electrocardiogram closely corresponds to the patient's actual heart electrical signal variations, aiding in accurate clinical diagnoses.ECG signals are affected by various artifact components, including highly influential EMG or motion artifacts, which can manifest simultaneously, randomly, or intermittently.An inflexible threshold level is not entirely appropriate for these cases.In this study, a solution integrating the wICA system with an adaptive filter model is proposed to overcome the limitation of a fixed threshold level.This combined system can provide the best prediction of artifact impacts to establish adaptive threshold values.Experimental results have shown that this new approach significantly improves the ability to remove artifacts from ECG records, with a correlation value of 0.9832 compared to the reference clean signal.