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A quantitative EEG method for detecting post clamp changes during carotid endarterectomy.

Meenakshi Mishra1, Muhammed Banday, Reza Derakhshani

  • 1Department of Computer Science and Electrical Engineering, School of Computing and Engineering, University of Missouri at Kansas City, MO 64110-2499, USA.

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This study developed a data-driven method to detect subtle EEG changes during carotid endarterectomy. The new technique identifies critical clamp-induced brain activity missed by visual inspection and other quantitative EEG metrics.

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Area of Science:

  • Neuroscience
  • Medical Engineering
  • Signal Processing

Background:

  • Carotid endarterectomy (CEA) requires careful monitoring of cerebral blood flow.
  • Subtle changes in electroencephalogram (EEG) during CEA can indicate potential complications.
  • Current methods, including visual inspection and some quantitative EEG (qEEG) metrics like r-sBSI and r-tBSI, may miss subtle clamp-induced alterations.

Purpose of the Study:

  • To analyze univariate and multivariate EEG spectral features for detecting subtle post-clamp changes during CEA.
  • To create a data-driven, multivariate classification system for rapid, real-time identification of small post-clamp EEG alterations.

Main Methods:

  • Analysis of intraoperative EEG records from 27 patients undergoing CEA without shunts.
  • Recording spectral energies of 10-second EEG snippets at 5-minute intervals from pre-clamp to post-clamp.
  • Utilizing receiver operating characteristic curves to assess EEG spectral band power for change detection.
  • Employing Fisher Linear Discriminant Analysis (LDA) for multivariate feature selection and classification.

Main Results:

  • Univariate analysis identified delta waves in non-frontal areas as key indicators of clamp-induced EEG changes.
  • Multivariate Fisher LDA achieved subject-independent classification with equal error rates as low as 32% for post-clamp vs. pre-clamp signals.
  • Clamp-induced EEG signatures were predominantly anterior, bilateral, and characterized by a strong delta rhythm.
  • Post-clamp EEG changes showed time-dependent saliency in both univariate and multivariate analyses.

Conclusions:

  • Automatic, data-driven feature extraction and classification can create subject-independent models to detect subtle post-clamp EEG changes.
  • These models can identify alterations potentially caused by minor cerebral blood flow perturbations, which are missed by visual and some qEEG methods.
  • The identified EEG signatures (anterior, bilateral, delta-dominant) provide insights into clamp-induced cerebral responses.