A wavelet-based adaptive filter for removing ECG interference in EMGdi signals.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Respiratory Physiology

Background:

• Diaphragmatic electromyogram (EMGdi) signals are vital for assessing respiratory function and diagnosing related diseases.
• Electrocardiographic (ECG) interference is a common artifact that corrupts EMGdi signals, hindering accurate analysis.
• Effective artifact removal is crucial for reliable interpretation of EMGdi data in clinical settings.

Purpose of the Study:

• To propose and evaluate an adaptive filter for removing ECG interference from EMGdi signals.
• To leverage wavelet theory for enhanced artifact suppression in biomedical signal processing.
• To validate the filter's performance on both simulated and clinical data.

Main Methods:

• Development of an adaptive filter based on wavelet theory.
• Application of power spectrum analysis to quantify the accuracy of the filtered EMGdi signal.
• Testing the filter on simulated ECG-corrupted EMGdi signals and real clinical data.

Main Results:

• Simulated results demonstrated an average error of 1.92% in the power spectral density of the extracted EMGdi signal compared to the original.
• The proposed filter effectively removed ECG artifacts from corrupted simulated EMGdi signals.
• Clinical data testing confirmed the method's efficiency in eliminating ECG interference from real-world EMGdi recordings.

Conclusions:

• The proposed wavelet-based adaptive filter is an effective tool for removing ECG interference from EMGdi signals.
• This method offers a reliable approach for signal preprocessing in the diagnosis and monitoring of respiratory diseases.
• The validated performance on clinical data suggests practical applicability in respiratory diagnostics.