Abstract
Permutation Entropy (PE) has recently been applied to characterize anesthetic-induced changes in the frontal electrical brain activity (EEG) during anesthesia. In this work we investigate the stability of PE as a means of identifying between the awake and anesthetized EEG over the entire duration of surgery under different anesthetic regimes and using a full set of EEG sensors. Average classification rates from 22 patients range between 98-99% (specificity, sensitivity and accuracy), when using information from whole-head EEG. The findings support the robustness of PE for discriminating 'awake' and 'anesthesia' throughout the entire surgery, independently of the anesthetic regime followed.
Original language | English |
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Pages (from-to) | 9-18 |
Number of pages | 10 |
Journal | Engineering Intelligent Systems |
Volume | 20 |
Issue number | 1-2 |
Publication status | Published - 1 Mar 2012 |
Externally published | Yes |
Keywords
- Anesthesia depth monitoring
- Electroencephalogram
- Permutation entropy
- Support vector machine