ArticlePhysiological measurement2020
Joint time-frequency analysis of dynamic cerebral autoregulation using generalized harmonic wavelets.
Article in Physiological measurement, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Time-Series Modeling and Forecasting of Cerebral Pressure-Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature.Sensors (Basel, Switzerland) · 2024Article
- Micro-Doppler Effect and Sparse Representation Analysis of Underwater Targets.Sensors (Basel, Switzerland) · 2023Article
- Dynamic cerebral autoregulation in postpartum individuals with and without preeclampsia.Pregnancy hypertension · 2023Article
- Reduced-order modeling and analysis of dynamic cerebral autoregulation via diffusion maps.Physiological measurement · 2023Article
- Cardiovascular and cerebrovascular health after pre-eclampsia: the Motherhealth prospective cohort study protocol.BMJ open · 2021Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo develop a joint time-frequency analysis technique based on generalized harmonic wavelets (GHWs) for dynamic cerebral autoregulation (DCA) performance quantification. APPROACH: We considered two groups of human subjects to develop and validate the method: 55 healthy volunteers and 35 stroke-free subjects with unilateral internal carotid artery stenosis (CAS). We determined the mean and coherence-weighted average of the phase shift (PS) of appropriately defined GHW-based transfer functions, based on data points over the joint time-frequency domain. We compared agreement of standard transfer function analysis (TFA) and GHW analyses in healthy subjects using Bland-Altman plots. We assessed sensitivity of each metric to detect the presumed side-to-side difference in DCA function in CAS subjects (with decreased PS on the occluded side), using McNemar's chi square test to compare each metric to the standard TFA approach. An alternative Morlet wavelet-based approach was also considered. MAIN
resultsThe GHW and TFA methods exhibited strong agreement in healthy subjects. Among CAS subjects, GHW metrics outperformed TFA and Morlet wavelet-based approaches in identifying expected side-to-side differences: TFA sensitivity was 40.0% (95%CI 23.9-57.9), Morlet 60.0% (95%CI 42.1-76.1), and GHW >70% for both metrics (GHW mean PS sensitivity 74.3, 95%CI 56.7-87.5, p = 0.0027 versus TFA; GHW coherence-weighted PS sensitivity 71.4, 95%CI 53.7-85.4, p = 0.0009 versus TFA). SIGNIFICANCE: In comparison to the widely used stationary Fourier transform-based TFA and to Morlet wavelet-based analysis, our data suggest that the GHW-based analysis performs better in identifying DCA asymmetry between the two cerebral hemispheres in patients with high grade unilateral carotid stenosis. Our method may provide enhanced confidence in employing DCA metrics as a sensitive diagnostic tool for detecting impaired DCA function in a variety of pathological settings.
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