Nonlinear Dynamic in the Eeg of Schizophrenic Patients and Its Variation With Mental Tasks

Dressel, M.; Ambühl-Braun, B.; Dünki, Rudolf M.; Meier, P. F.; Elbert, T.

doi:10.1142/9789812793782_0049

Cited by 2 publications

(6 citation statements)

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“…This result is in agreement with findings from an earlier study [2] and with previous results from our sample [18, 19]. The δ power seems to be the best spectral measure because it appears in two combinations.…”

Section: Application To the Problem Discriminating Eeg Statessupporting

confidence: 94%

“…Our clinical sample consists of 30 persons from two clinical groups evaluated at four mental states ([ 18 ], see also Section 4.2 ). So we have k = 4 and # n = 30.…”

Section: Recapitulation Of the F -Ratio Testmentioning

confidence: 99%

“…So we have k = 4 and # n = 30. Because the mental states have shown fixed effects in previous studies [ 18 , 19 ], the simulated signals were offset by four fixed different levels. The amount of the offset values is not relevant, however, because the offset is fixed and the F- ratio test is set up to test for differences between the two groups.…”

Section: Recapitulation Of the F -Ratio Testmentioning

confidence: 99%

“…An earlier study conducted with our proband samples (cf. below) revealed a significant difference between the two samples but only for a specific mental task [ 18 ]. While the EEG of the controls showed a drastic decrease in dimensionality, the EEG of the patients did not exhibit any pecularity.…”

Section: Application To the Problem Discriminating Eeg Statesmentioning

confidence: 99%

“…This provided measures like α - or δ -power, that is, the spectral power from the so-called α ( 8–12 Hz) and δ (1–5 Hz) frequency band. A complete description of the proband samples, conditions, and technical settings is given elsewhere [ 18 , 19 ]. With our experimental setup, the model consists of four fixed conditions (i.e., the four mental tasks) and two groups with 15 persons (i.e., patients and controls).…”

Section: Application To the Problem Discriminating Eeg Statesmentioning

confidence: 99%

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F-Ratio Test and Hypothesis Weighting: A Methodology to Optimize Feature Vector Size

Dünki

Dressel

2011

Journal of Biophysics

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Reducing a feature vector to an optimized dimensionality is a common problem in biomedical signal analysis. This analysis retrieves the characteristics of the time series and its associated measures with an adequate methodology followed by an appropriate statistical assessment of these measures (e.g., spectral power or fractal dimension). As a step towards such a statistical assessment, we present a data resampling approach. The techniques allow estimating σ 2(F), that is, the variance of an F-value from variance analysis. Three test statistics are derived from the so-called F-ratio σ 2(F)/F 2. A Bayesian formalism assigns weights to hypotheses and their corresponding measures considered (hypothesis weighting). This leads to complete, partial, or noninclusion of these measures into an optimized feature vector. We thus distinguished the EEG of healthy probands from the EEG of patients diagnosed as schizophrenic. A reliable discriminance performance of 81% based on Taken's χ, α-, and δ-power was found.

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Section: Application To the Problem Discriminating Eeg Statessupporting

confidence: 94%

“…Our clinical sample consists of 30 persons from two clinical groups evaluated at four mental states ([ 18 ], see also Section 4.2 ). So we have k = 4 and # n = 30.…”

Section: Recapitulation Of the F -Ratio Testmentioning

confidence: 99%

Section: Recapitulation Of the F -Ratio Testmentioning

confidence: 99%

Section: Application To the Problem Discriminating Eeg Statesmentioning

confidence: 99%

Section: Application To the Problem Discriminating Eeg Statesmentioning

confidence: 99%

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