NDLI: Analysis of complexity based EEG features for the diagnosis of Alzheimer's disease

Content Provider	IEEE Xplore Digital Library
Author	Staudinger, T. Polikar, R.
Copyright Year	2011
Description	Author affiliation: Signal Processing and Pattern Recognition Laboratory, Dept. of Electrical and Computer Eng. at Rowan University, Glassboro, NJ 08028, USA (Staudinger, T.; Polikar, R.)
Abstract	As life expectancy increases, particularly in the developed world, so does the prevalence of Alzheimer's Disease (AD). AD is a neurodegenerative disorder characterized by neu-rofibrillary plaques and tangles in the brain that leads to neu-ronal death and dementia. Early diagnosis of AD is still a major unresolved health concern: several biomarkers are being investigated, among which the electroencephalogram (EEG) provides the only option for an electrophysiological information. In this study, EEG signals obtained from 161 subjects — 79 with AD, and 82 age-matched controls (CN) — are analyzed using several nonlinear signal complexity measures. These measures include: Hi-guchi fractal dimension (HFD), spectral entropy (SE), spectral centroid (SC), spectral roll-off (SR), and zero-crossing rate (ZCR). HFD is a quantitative measure of time series complexity derived from fractal theory. Among spectral measures, SE measures the level of disorder in the spectrum, SC is a measure of spectral shape, and SR is frequency sample below which a specified percent of the spectral magnitude distribution is contained. Lastly, ZCR is simply the rate at which the signal changes signs. A t-test was first applied to determine those features that provide significant differences between the groups. Those features were then used to train a neural network. The classification accuracies ranged from 60–66%, suggesting they contain some discriminatory information; however, not enough to be clinically useful alone. Combining these features and training a support vector machine (SVM) resulted in a diagnostic accuracy of 78%, indicating that these feature carry complementary information.
Starting Page	2033
Ending Page	2036
File Size	432075
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090374
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Complexity theory Electroencephalography Entropy Accuracy Fractals Alzheimer's disease Zero-Crossing Rate EEG Higuchi Fractal Dimension Spectral Entropy Spectral Centroid Spectral Roll-Off
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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