Comput Math Methods Med - Spectral asymmetry and Higuchi's fractal dimension measures of depression electroencephalogram.

Tópicos

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Resumo

This study was aimed to compare two electroencephalogram (EEG) analysis methods, spectral asymmetry index (SASI) and Higuchi's fractal dimension (HFD), for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control subjects were used as a database for calculations. SASI values were positive for depressive and negative for control group (P < 0.05). SASI provided the true detection rate of 88% in the depressive and 82% in the control group. The calculated HFD values detected a small (3%) increase with depression (P < 0.05). HFD provided the true detection rate of 94% in the depressive group and 76% in the control group. The rate of correct indication in the both groups was 85% using SASI or HFD. Statistically significant variations were not revealed between hemispheres (P > 0.05). The results indicated that the linear EEG analysis method SASI and the nonlinear HFD method both demonstrated a good sensitivity for detection of characteristic features of depression in a single-channel EEG.

Resumo Limpo

studi aim compar two electroencephalogram eeg analysi method spectral asymmetri index sasi higuchi fractal dimens hfd detect depress linear sasi method base evalu balanc power two eeg frequenc band one channel select higher lower alpha band spectrum maximum nonlinear hfd method calcul fractal dimens direct time domain rest eeg signal depress patient control subject use databas calcul sasi valu posit depress negat control group p sasi provid true detect rate depress control group calcul hfd valu detect small increas depress p hfd provid true detect rate depress group control group rate correct indic group use sasi hfd statist signific variat reveal hemispher p result indic linear eeg analysi method sasi nonlinear hfd method demonstr good sensit detect characterist featur depress singlechannel eeg

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