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 language | English |
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Pages (from-to) | 77-88 |
Number of pages | 12 |
Journal | Clinical EEG and Neuroscience |
Volume | 45 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Externally published | Yes |
Keywords
- anesthesia
- awareness
- electroencephalogram
- granger causality
- neural network classifier