A novel physiological signal denoising method coupled with multispectral adaptive wavelet denoising(MAWD) and unsupervised source counting algorithm(USCA)

In order to improve the quality of physiological signals, a combined study of blind source separation and wavelet thresholding methods was conducted, resulting in the proposal of a multispectral adaptive wavelet denoising (MAWD) method. This method was employed in conjunction with an improved unsupervised source counting algorithm (USCA). To evaluate the effectiveness of the proposed approach, three methods were used to calculate signal-to-noise ratio (SNR) and root mean square error (RMSE): soft thresholding, hard thresholding, and adaptive thresholding. The results demonstrated that the proposed method exhibited strong applicability under soft thresholding. Specifically, compared to hard thresholding, the enhanced signal using soft thresholding showed an approximately 44.2% increase in SNR and a 28.8% decrease in RMSE, along with a 1.4% reduction in processing time. Moreover, when compared to adaptive thresholding, soft thresholding exhibited approximately 706% improvement in SNR, a 16.7% decrease in RMSE, and a 3.0% reduction in processing time. Multiple experiments were conducted to determine the optimal peak detection threshold range for USCA, which was found to be within the interval [0.001, 0.0001]. This range facilitated the separation of more sources, thereby enhancing the separation effectiveness and accuracy. To substantiate the effectiveness of the USCA method, tests were conducted on publicly available datasets of EMG, ECG, and EEG signals, all of which consistently demonstrated the advantages of this approach.

Data Availability

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