EEG under anesthesia--feature extraction with TESPAR.
We investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram (EEG) recordings. We employed Time Encoded Signal Processing And Recognition (TESPAR), a time-domain signal processing technique, in combination with multi-layer perceptrons to identify DOA levels. The presented system learns to discriminate between five DOA classes assessed by human experts whose judgements were based on EEG mid-latency auditory evoked potentials (MLAEPs) and clinical observations. We found that our system closely mimicked the behavior of the human expert, thus proving the utility of the method. Further analyses on the features extracted by our technique indicated that information related to DOA is mostly distributed across frequency bands and that the presence of high frequencies (> 80 Hz), which reflect mostly muscle activity, is beneficial for DOA detection.
Moca
Vasile V
VV
Romanian Institute of Science and Technology, Center for Cognitive and Neural Studies (Coneural), Str. Cireşilor nr. 29, 400487 Cluj-Napoca, Romania. moca@coneural.org
Scheller
Bertram
B
Mureşan
Raul C
RC
Daunderer
Michael
M
Pipa
Gordon
G
D000465:Algorithms; D000768:Anesthesia, General / Q000379:methods; D018681:Anesthetics, General / Q000008:administration & dosage*; D001921:Brain / Q000187:drug effects* / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000187:drug effects* / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
D018681:Anesthetics, General