کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
3047310 1185055 2008 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی عصب شناسی
پیش نمایش صفحه اول مقاله
Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?
چکیده انگلیسی

ObjectiveIt has been shown that a new procedure (implicit function as squashing time, IFAST) based on artificial neural networks (ANNs) is able to compress eyes-closed resting electroencephalographic (EEG) data into spatial invariants of the instant voltage distributions for an automatic classification of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) subjects with classification accuracy of individual subjects higher than 92%.MethodsHere we tested the hypothesis that this is the case also for the classification of individual normal elderly (Nold) vs. MCI subjects, an important issue for the screening of large populations at high risk of AD. Eyes-closed resting EEG data (10–20 electrode montage) were recorded in 171 Nold and in 115 amnesic MCI subjects. The data inputs for the classification by IFAST were the weights of the connections within a nonlinear auto-associative ANN trained to generate the instant voltage distributions of 60-s artifact-free EEG data.ResultsThe most relevant features were selected and coincidently the dataset was split into two halves for the final binary classification (training and testing) performed by a supervised ANN. The classification of the individual Nold and MCI subjects reached 95.87% of sensitivity and 91.06% of specificity (93.46% of accuracy).ConclusionsThese results indicate that IFAST can reliably distinguish eyes-closed resting EEG in individual Nold and MCI subjects.SignificanceIFAST may be used for large-scale periodic screening of large populations at risk of AD and personalized care.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Clinical Neurophysiology - Volume 119, Issue 7, July 2008, Pages 1534–1545
نویسندگان
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