Neural network-based classification of anesthesia/awareness using granger causality features

Nicoletta Nicolaou, Julius Georgiou

Research output: Contribution to journalArticlepeer-review

Abstract

This article investigates the signal processing part of a future system for monitoring awareness during surgery. The system uses features from the patients' electrical brain activity (EEG) to discriminate between "anesthesia" and "awareness." We investigate the use of a neural network classifier and Granger causality (GC) features for this purpose. GC captures anesthetic-induced changes in the causal relationships between pairs of signals from different brain areas. The differences in the pairwise causality estimated from the EEG activity are used as features for subsequent classification between "awake" and "anesthetized" states. EEG data from 31 subjects obtained during surgery and maintenance of anesthesia with propofol, sevoflurane, or desflurane, are classified using a neural network with one layer of hidden units. An average accuracy of 96% is obtained.

Original languageEnglish
Pages (from-to)77-88
Number of pages12
JournalClinical EEG and Neuroscience
Volume45
Issue number2
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • anesthesia
  • awareness
  • electroencephalogram
  • granger causality
  • neural network classifier

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