EEG filtering based on blind source separation (BSS) improves detection of Alzheimer disease
نویسندگان
چکیده
Objective: Improvement of detection of Alzheimer disease (AD) by filtering of EEG data using blind source separation (BSS) and projection of components which are possibly sensitive to cortical neuronal impairment found in early stages of AD. Method: Artifact-free 20 s intervals of raw resting EEG recordings from 22 mild AD patients and 38 age-matched controls were decomposed into spatio-temporally decorrelated components using BSS algorithm "AMUSE". Filtered EEG was obtained by back projection of components with the highest linear predictability. Relative power of filtered data in delta, theta, alpha1, alpha2, beta1, and beta 2 bands were processed with Linear Discriminant Analysis (LDA). Results: Preprocessing improved the percentage of correctly classified patients and controls computed with jack-knifing cross-validation from 59 to 73% and from 76 to 84%, correspondingly. Conclusions: The proposed approach can significantly improve the sensitivity and specificity of EEG based AD diagnosis and may have potential for improvement of EEG classification in other clinical areas or fundamental research. Significance: Since the patients with AD should be identified during large scale screening, inexpensive tools are highly needed. The developed method is quite general and flexible, allowing for various extensions. * Abstract
منابع مشابه
EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease.
OBJECTIVE Development of an EEG preprocessing technique for improvement of detection of Alzheimer's disease (AD). The technique is based on filtering of EEG data using blind source separation (BSS) and projection of components which are possibly sensitive to cortical neuronal impairment found in early stages of AD. METHODS Artifact-free 20s intervals of raw resting EEG recordings from 22 pati...
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