Identifying stable phase coupling associated with cerebral autoregulation using the synchrosqueezed cross-wavelet...

Area of Science:

• Signal Processing
• Biomedical Engineering
• Time-Frequency Analysis

Background:

• Understanding signal interactions is crucial in various scientific fields.
• Traditional methods may struggle to precisely identify stable phase coupling in complex time-frequency data.
• Cerebral autoregulation analysis relies on accurately assessing relationships between physiological signals.

Purpose of the Study:

• To introduce a novel method for identifying stable phase coupling between two signals.
• To enhance the analysis of time-frequency representations using wavelet transforms.
• To apply the new method to biosignal analysis in the context of cerebral autoregulation.

Main Methods:

• Utilizing the cross-wavelet transform to map phase coupling between signals.
• Employing synchrosqueezing to isolate and collect stable phase coupling information.
• Developing the synchrosqueezed cross-wavelet transform (Synchro-CrWT) method.

Main Results:

• The Synchro-CrWT method successfully identifies stable phase coupling regimes.
• Demonstrated efficacy on a synthetic signal for validation.
• Applied to analyze relationships between biosignals relevant to cerebral autoregulation.

Conclusions:

• The Synchro-CrWT offers a robust approach for detecting stable phase coupling.
• This method improves the analysis of signal dynamics in time-frequency domains.
• It holds potential for advancing the study of physiological systems like cerebral autoregulation.