Bioelectric signal detrending using smoothness prior approach.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Physiological Monitoring

Background:

• Bioelectric signals (electromyogram, electrocardiogram) are susceptible to low-frequency trending interferences.
• Accurate analysis of bioelectric signals requires effective removal of these artifacts.
• Existing detrending methods may compromise signal quality or fail to remove all interferences.

Purpose of the Study:

• To propose and evaluate an advanced detrending method based on the Smoothness Prior Approach (SPA).
• To compare the performance of SPA against a conventional high-pass Butterworth filter for bioelectric signal detrending.
• To assess the method's effectiveness in preserving critical signal features.

Main Methods:

• Implementation of the Smoothness Prior Approach (SPA) for signal detrending.
• Application of SPA to electromyogram (EMG) and electrocardiogram (ECG) signals from the MIT-BIH database and experimental recordings.
• Comparison with a high-pass Butterworth filter using correlation coefficient (CC) and root mean square error (RMSE) metrics.

Main Results:

• The proposed SPA method demonstrated superior performance in reducing trending interferences compared to the high-pass Butterworth filter.
• SPA effectively preserved the desired frequency content of the EMG and ECG signals.
• Quantitative evaluation using CC and RMSE confirmed the superiority of the SPA method.

Conclusions:

• The Smoothness Prior Approach (SPA) is a highly effective method for detrending bioelectric signals.
• SPA offers significant advantages over conventional filtering techniques for artifact removal.
• SPA shows promise as a valuable tool for bioelectric signal processing and analysis.