Investigation of Cerebral Autoregulation Using Time-Frequency Transformations

Area of Science:

• Biomedical Engineering
• Neuroscience
• Signal Processing

Background:

• Systemic and cerebral hemodynamics are crucial in neurosurgical pathologies.
• Effective treatment relies on understanding cerebral circulation regulation.
• Current methods may lack sensitivity in assessing autoregulation.

Purpose of the Study:

• To develop and validate a new signal processing method for assessing cerebral autoregulation.
• To analyze hemodynamics in neurosurgical patients using advanced techniques.
• To improve the real-time monitoring of cerebral circulation regulation.

Main Methods:

• Continuous wavelet transform (CWT) applied to systemic arterial pressure (SAP) and middle cerebral artery blood flow velocity (MCA-BFV) signals.
• Non-invasive recording using photoplethysmography (PPG) and transcranial Doppler ultrasonography (TCD).
• Cross-wavelet spectrum analysis to determine wavelet coherence and phase shift in the Mayer wave frequency range.

Main Results:

• The CWT-based method effectively characterizes cerebral autoregulation mechanisms.
• Real-time analysis in the Mayer wave range allows for determining autoregulation status.
• The proposed method demonstrated higher sensitivity and better time-frequency localization compared to short-time Fourier transform (STFT).

Conclusions:

• The novel wavelet transform method provides a sensitive tool for assessing cerebral autoregulation.
• This technique aids in addressing fundamental and clinical problems in neurosurgery.
• It enables effective treatment strategies for patients with various pathologies.