| Literature DB >> 35443895 |
Autumn Williams1, Yinuo Zeng1, Ziwei Li1, Nitish Thakor1, Romergryko G Geocadin2, Jay Bronder2, Nirma Carballido Martinez2, Eva K Ritzl2, Sung-Min Cho3.
Abstract
Objective assessment of the brain's responsiveness in comatose patients on Extracorporeal Membrane Oxygenation (ECMO) support is essential to clinical care, but current approaches are limited by subjective methodology and inter-rater disagreement. Quantitative electroencephalogram (EEG) algorithms could potentially assist clinicians, improving diagnostic accuracy. We developed a quantitative, stimulus-based algorithm to assess EEG reactivity features in comatose patients on ECMO support. Patients underwent a stimulation protocol of increasing intensity (auditory, peripheral, and nostril stimulation). A total of 129 20-s EEG epochs were collected from 24 patients (age [Formula: see text], 10 females, 14 males) on ECMO support with a Glasgow Coma Scale[Formula: see text]8. EEG reactivity scores ([Formula: see text]-scores) were calculated using aggregated spectral power and permutation entropy for each of five frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Parameter estimation techniques were applied to [Formula: see text]-scores to identify properties that replicate the decision process of experienced clinicians performing visual analysis. Spectral power changes from audio stimulation were concentrated in the [Formula: see text] band, whereas peripheral stimulation elicited an increase in spectral power across multiple bands, and nostril stimulation changed the entropy of the [Formula: see text] band. The findings of this pilot study on [Formula: see text]-score lay a foundation for a future prediction tool with clinical applications.Entities:
Keywords: ECMO; EEG reactivity; Quantitative EEG; coma; disorder of consciousness; regression analysis
Mesh:
Year: 2022 PMID: 35443895 PMCID: PMC9436243 DOI: 10.1142/S0129065722500253
Source DB: PubMed Journal: Int J Neural Syst ISSN: 0129-0657 Impact factor: 6.325