Median frequency revisited - An approach to improve a classic spectral electroencephalographic parameter for the separation of consciousness from unconsciousness

University of Duisburg-Essen, Essen, North Rhine-Westphalia, Germany
Anesthesiology (Impact Factor: 6.17). 10/2007; 107(3):397-405. DOI: 10.1097/01.anes.0000278904.63884.4c
Source: PubMed

ABSTRACT In the past, several electroencephalographic parameters have been presented and discussed with regard to their reliability in discerning consciousness from unconsciousness. Some of them, such as the median frequency and spectral edge frequency, are based on classic spectral analysis, and it has been demonstrated that they are of limited capacity in differing consciousness and unconsciousness.
A generalized approach based on the Fourier transform is presented to improve the performance of electroencephalographic parameters with respect to the separation of consciousness from unconsciousness. Electroencephalographic data from two similar clinical studies (for parameter development and evaluation) in adult patients undergoing general anesthesia with sevoflurane or propofol are used. The study period was from induction of anesthesia until patients followed command after surgery and includes a reduction of the hypnotic agent after tracheal intubation until patients followed command. Prediction probability was calculated to assess the ability of the parameters to separate consciousness from unconsciousness.
On the basis of the training set of 40 patients, a new spectral parameter called weighted spectral median frequency was designed, achieving a prediction probability of 0.82 on the basis of the "classic" electroencephalographic frequency range up to 30 Hz. Next, in the evaluation data set, the prediction probability was 0.79, which is higher than the prediction probability of median frequency (0.58) or spectral edge frequency (0.59) and the Bispectral Index (0.68) as calculated from the same data set.
A more general approach of the design of spectral parameters leads to a new electroencephalographic spectral parameter that separates consciousness from unconsciousness significantly better than the Bispectral Index.

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Available from: Denis Jordan, Jun 21, 2015
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