Autori: Moca VV, Scheller B, Muresan RC, Daunderer M, Pipa G

Editorial: Computer Methods and Programs in Biomedicine, 95, p.191-202, 2009.

Rezumat:

We investigated the problem of automatic depth of anesthesia (DOA) estimation from electroencephalogram
(EEG) recordings. We employed TESPAR (Time Encoded Signal Processing
And Recognition), 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 (MLAEP) 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.

Cuvinte cheie: Depth of anesthesia, EEG, MLP, TESPAR, MLAEP

URL: http://www.raulmuresan.ro/Papers/Moca%20et%20al.%202009.pdf