EEG under anesthesia-Feature extraction with TESPAR

  • Authors:

  • Vasile V. Moca;Bertram Scheller;Raul C. Mureşan;Michael Daunderer;Gordon Pipa

  • Affiliations:

  • Romanian Institute of Science and Technology, Center for Cognitive and Neural Studies (Coneural), Str. Cireşilor nr. 29, 400487 Cluj-Napoca, Romania;Clinic for Anesthesiology, Johann Wolfgang Goethe University, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany;Romanian Institute of Science and Technology, Center for Cognitive and Neural Studies (Coneural), Str. Cireşilor nr. 29, 400487 Cluj-Napoca, Romania and Max Planck Institute for Brain Researc ...;Clinic for Anesthesiology, Ludwig Maximilians University, Nussbaumstraíe 20, 80336 Munich, Germany;Max Planck Institute for Brain Research, Deutschordenstraíe 46, 60528 Frankfurt am Main, Germany and Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, Max-von-Laue- ...

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2009

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Abstract

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 (80Hz), which reflect mostly muscle activity, is beneficial for DOA detection.