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Median frequency revisited: an approach to improve a classic spectral electroencephalographic parameter for the separation of consciousness from unconsciousness.
Anesthesiology. 2007 Sep; 107(3):397-405.A

Abstract

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Authors+Show Affiliations

Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Campus Duisburg, Germany.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Randomized Controlled Trial
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

17721241

Citation

Jordan, Denis, et al. "Median Frequency Revisited: an Approach to Improve a Classic Spectral Electroencephalographic Parameter for the Separation of Consciousness From Unconsciousness." Anesthesiology, vol. 107, no. 3, 2007, pp. 397-405.
Jordan D, Stockmanns G, Kochs EF, et al. Median frequency revisited: an approach to improve a classic spectral electroencephalographic parameter for the separation of consciousness from unconsciousness. Anesthesiology. 2007;107(3):397-405.
Jordan, D., Stockmanns, G., Kochs, E. F., & Schneider, G. (2007). Median frequency revisited: an approach to improve a classic spectral electroencephalographic parameter for the separation of consciousness from unconsciousness. Anesthesiology, 107(3), 397-405.
Jordan D, et al. Median Frequency Revisited: an Approach to Improve a Classic Spectral Electroencephalographic Parameter for the Separation of Consciousness From Unconsciousness. Anesthesiology. 2007;107(3):397-405. PubMed PMID: 17721241.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Median frequency revisited: an approach to improve a classic spectral electroencephalographic parameter for the separation of consciousness from unconsciousness. AU - Jordan,Denis, AU - Stockmanns,Gudrun, AU - Kochs,Eberhard F, AU - Schneider,Gerhard, PY - 2007/8/28/pubmed PY - 2007/10/27/medline PY - 2007/8/28/entrez SP - 397 EP - 405 JF - Anesthesiology JO - Anesthesiology VL - 107 IS - 3 N2 - BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SN - 0003-3022 UR - https://www.unboundmedicine.com/medline/citation/17721241/Median_frequency_revisited:_an_approach_to_improve_a_classic_spectral_electroencephalographic_parameter_for_the_separation_of_consciousness_from_unconsciousness_ L2 - https://pubs.asahq.org/anesthesiology/article-lookup/doi/10.1097/01.anes.0000278904.63884.4c DB - PRIME DP - Unbound Medicine ER -