TY - GEN
T1 - Permutation entropy for discriminating 'conscious' and 'unconscious' state in general anesthesia
AU - Nicolaou, Nicoletta
AU - Houris, Saverios
AU - Alexandrou, Pandelitsa
AU - Georgiou, Julius
PY - 2011/11/2
Y1 - 2011/11/2
N2 - Brain-Computer Interfaces (BCIs) are devices offering alternative means of communication when conventional means are permanently, or nonpermanently, impaired. The latter is commonly induced in general anesthesia and is necessary for the conduction of the surgery. However, in some cases it is possible that the patient regains consciousness during surgery, but cannot directly communicate this to the anesthetist due to the induced muscle paralysis. Therefore, a BCI-based device that monitors the spontaneous brain activity and alerts the anesthetist is an essential addition to routine surgery. In this paper the use of Permutation Entropy (PE) as a feature for 'conscious' and 'unconscious' brain state classification for a BCI-based anesthesia monitor is investigated. PE is a linear complexity measure that tracks changes in spontaneous brain activity resulting from the administration of anesthetic agents. The overall classification performance for 10 subjects, as assessed with a linear Support Vector Machine, exceeds 95%, indicating that PE is an appropriate feature for such a monitoring device.
AB - Brain-Computer Interfaces (BCIs) are devices offering alternative means of communication when conventional means are permanently, or nonpermanently, impaired. The latter is commonly induced in general anesthesia and is necessary for the conduction of the surgery. However, in some cases it is possible that the patient regains consciousness during surgery, but cannot directly communicate this to the anesthetist due to the induced muscle paralysis. Therefore, a BCI-based device that monitors the spontaneous brain activity and alerts the anesthetist is an essential addition to routine surgery. In this paper the use of Permutation Entropy (PE) as a feature for 'conscious' and 'unconscious' brain state classification for a BCI-based anesthesia monitor is investigated. PE is a linear complexity measure that tracks changes in spontaneous brain activity resulting from the administration of anesthetic agents. The overall classification performance for 10 subjects, as assessed with a linear Support Vector Machine, exceeds 95%, indicating that PE is an appropriate feature for such a monitoring device.
KW - anesthesia monitor
KW - Brain-Computer Interface
KW - electroencephalogram
KW - Permutation Entropy
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=80055055214&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23957-1_32
DO - 10.1007/978-3-642-23957-1_32
M3 - Conference contribution
AN - SCOPUS:80055055214
SN - 9783642239564
T3 - IFIP Advances in Information and Communication Technology
SP - 280
EP - 288
BT - Engineering Applications of Neural Networks - 12th INNS EANN-SIG International Conference, EANN 2011 and 7th IFIP WG 12.5 International Conference, AIAI 2011, Proceedings
T2 - 12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks, EANN 2011
Y2 - 15 September 2011 through 18 September 2011
ER -