TY - GEN
T1 - Autoregressive model order estimation criteria for monitoring awareness during anaesthesia
AU - Nicolaou, Nicoletta
AU - Georgiou, Julius
PY - 2013/12/1
Y1 - 2013/12/1
N2 - This paper investigates the use of autoregressive (AR) model order estimation criteria for monitoring awareness during anaesthesia. The Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC) were applied to electroencephalogram (EEG) data from 29 patients, obtained during surgery, to estimate the optimum multivariate AR model order. Maintenance of anaesthesia was achieved with propofol, desflurane or sevoflurane. The optimum orders estimated from the BIC reliably decreased during anaesthetic-induced unconsciousness, as opposed to AIC estimates, and, thus, successfully tracked the loss of awareness. This likely reflects the decrease in the complexity of the brain activity during anaesthesia. In addition, AR order estimates sharply increased for diathermy-contaminated EEG segments. Thus, the BIC could provide a simple and reliable means of identifying awareness during surgery, as well as automatic exclusion of diathermy-contaminated EEG segments.
AB - This paper investigates the use of autoregressive (AR) model order estimation criteria for monitoring awareness during anaesthesia. The Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC) were applied to electroencephalogram (EEG) data from 29 patients, obtained during surgery, to estimate the optimum multivariate AR model order. Maintenance of anaesthesia was achieved with propofol, desflurane or sevoflurane. The optimum orders estimated from the BIC reliably decreased during anaesthetic-induced unconsciousness, as opposed to AIC estimates, and, thus, successfully tracked the loss of awareness. This likely reflects the decrease in the complexity of the brain activity during anaesthesia. In addition, AR order estimates sharply increased for diathermy-contaminated EEG segments. Thus, the BIC could provide a simple and reliable means of identifying awareness during surgery, as well as automatic exclusion of diathermy-contaminated EEG segments.
KW - Anaesthesia
KW - AR model order estimation
KW - Awareness
KW - EEG
UR - http://www.scopus.com/inward/record.url?scp=84894106443&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41142-7_8
DO - 10.1007/978-3-642-41142-7_8
M3 - Conference contribution
AN - SCOPUS:84894106443
SN - 9783642411410
T3 - IFIP Advances in Information and Communication Technology
SP - 71
EP - 80
BT - Artificial Intelligence Applications and Innovations - 9th IFIPWG 12.5 International Conference, AIAI 2013, Proceedings
T2 - 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013
Y2 - 30 September 2013 through 2 October 2013
ER -