Empirical Blaschke Mode decomposition: Algorithm and application.

Area of Science:

• Signal Processing
• Engineering
• Biomedical Engineering

Background:

• Complex signals in biomedical and aerospace engineering often contain periodic pulse components vital for monitoring.
• Accurate extraction of these periodic features is a significant challenge in signal analysis.

Purpose of the Study:

• To introduce a novel signal processing method, Empirical Blaschke Mode Decomposition (EBMD), for robust extraction of periodic pulse features.
• To validate the efficacy of EBMD in diverse applications including mechanical vibrations, EEG, and ECG signals.

Main Methods:

• The Empirical Blaschke Mode Decomposition (EBMD) method utilizes the Blaschke Transform for quasi-periodic feature capture.
• A unimodal pre-segmentation strategy defines relevant spectrum bands, followed by group sparse filter bank decomposition.
• A periodic frequency similarity fusion strategy prevents over-decomposition by consolidating modes into eigenmode functions.

Main Results:

• EBMD successfully separates signals, extracts critical periodic pulse features, and reduces noise in simulated and real-world data.
• Validation across mechanical vibration analysis, EEG denoising, and ECG signal separation demonstrates EBMD's versatility and effectiveness.

Conclusions:

• Empirical Blaschke Mode Decomposition (EBMD) offers a powerful new approach for analyzing complex signals with periodic components.
• The method shows significant potential for improving monitoring and analysis in biomedical and aerospace engineering fields.