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Joint time-frequency analysis of dynamic cerebral autoregulation using generalized harmonic wavelets.

E C Miller1,2, K R M Dos Santos3,2, R S Marshall1

  • 1Neurology-Stroke Division, Neurological Institute of New York, Columbia University Irving Medical Center, New York, NY, United States of America.

Physiological Measurement
|January 31, 2020
PubMed
Summary

Generalized harmonic wavelets (GHWs) offer improved detection of dynamic cerebral autoregulation (DCA) asymmetry in carotid stenosis patients. This novel GHW analysis enhances diagnostic confidence for impaired brain blood flow regulation.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Dynamic cerebral autoregulation (DCA) is crucial for maintaining stable brain blood flow.
  • Impaired DCA is associated with cerebrovascular diseases like carotid artery stenosis (CAS).
  • Accurate quantification of DCA performance is essential for diagnosis and management.

Purpose of the Study:

  • To develop and validate a novel joint time-frequency analysis technique using generalized harmonic wavelets (GHWs) for DCA performance quantification.
  • To compare the efficacy of GHW analysis against standard transfer function analysis (TFA) and Morlet wavelet analysis in detecting DCA asymmetry.

Main Methods:

  • A new method based on GHWs was developed to analyze joint time-frequency properties of physiological signals.
  • The technique was validated in 55 healthy volunteers and 35 patients with unilateral internal carotid artery stenosis (CAS).
  • Phase shift (PS) metrics derived from GHW transfer functions were compared with TFA and Morlet wavelet approaches using Bland-Altman plots and McNemar's test.

Main Results:

  • GHW and TFA methods showed strong agreement in healthy subjects.
  • In CAS patients, GHW metrics demonstrated superior sensitivity in detecting expected side-to-side differences in DCA function compared to TFA and Morlet wavelets.
  • GHW mean PS sensitivity was 74.3% and coherence-weighted PS sensitivity was 71.4%, significantly outperforming TFA (40.0%) and Morlet (60.0%).

Conclusions:

  • The GHW-based analysis is more effective than traditional TFA and Morlet wavelet methods for identifying hemispheric DCA asymmetry in patients with high-grade unilateral carotid stenosis.
  • This advanced GHW technique offers enhanced confidence in using DCA metrics as a sensitive diagnostic tool for detecting impaired brain blood flow regulation in various pathological conditions.