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Comment
. 2013 Jan 26;381(9863):289-91.
doi: 10.1016/S0140-6736(13)60125-7.

Reanalysis of "Bedside detection of awareness in the vegetative state: a cohort study"

Comment

Reanalysis of "Bedside detection of awareness in the vegetative state: a cohort study"

Andrew M Goldfine et al. Lancet. .
No abstract available

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Figures

Figure 1
Figure 1
Time and frequency domain representations of the EEG of a typical normal (N2) and patient (P13) who had similar classification rates in Cruse et al. (75% and 78%, respectively; Webappendix for methods). A. Laplacian-montaged EEG of the first trial of hand and toe block 1. The 25 channels used in Cruse et al. are shown. Note high frequency activity in P13 that differs between the trials. B. Spectra of the EEG calculated from each block, color-coded by block type, for the same subjects as Panel A. Rest period is data 1·5 to 0 seconds pre-tone, and task period is data 0·5 to 2·0 seconds post-tone. Channels displayed include extreme left, midline and extreme right of the 25 channels shown in Panel A. I-bar symbol in each plot of Panel B represents average 95% confidence limits for the spectra (by jackknife). If trials were independent, the spectral estimates from each block should agree with each other, up to the confidence limits of each estimate. This holds for the data from normals (left) but not patients (right).

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References

    1. Cruse D, Chennu S, Chatelle C, et al. Bedside detection of awareness in the vegetative state: a cohort study. Lancet. 2011;378:2088–2094. - PubMed
    1. Schomer DL, da Silva FL. Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Sixth. Philadelphia, PA: Lippincott Williams & Wilkins; 2010.
    1. Whitham EM, Pope KJ, Fitzgibbon SP, et al. Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG. Clin Neurophysiol. 2007;118:1877–1888. - PubMed
    1. Bishop CM. Pattern Recognition and Machine Learning. 1st ed. New York, NY: Springer; 2007. 2006. Corr. 2nd printing.
    1. Noble WS. What is a support vector machine? Nature Biotechnology. 2006;24:1565–1567. - PubMed