title: Analysis of complexity based EEG features for the diagnosis of Alzheimer's disease.
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 neurofibrillary plaques and tangles in the brain that leads to neuronal 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: Higuchi 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.
authors.affiliation: Signal Processing and Pattern Recognition Laboratory, Dept of Electrical and Computer Eng, Rowan University, Glassboro, NJ 08028, USA. staudi25@students.rowan.edu
authors.forename: Tyler
authors.initials: T
authors.lastname: Staudinger
authors.forename: Robi
authors.initials: R
authors.lastname: Polikar
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000503:physiopathology*; D001185:Artificial Intelligence; D001921:Brain / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity