Dynamical Nonstationarity Analysis of Resting EEGs in Alzheimer's Disease

The understanding of nonstationarity, from both a dynamical and a statistical point of view, has turned from a constraint on application of a specific type of analysis (e.g. spectral analysis), into a new insight into complex system behavior. The application of nonstationarity detection in an EEG time series plays an important role in the characterization of brain processes and the prediction of brain state and behavior such as seizure prediction. In this study, we report a very significant difference in the mean stationarity duration of an EEG over the frontal and temporal regions of the brain, comparing 22 healthy subjects and 16 patients with mild Alzheimer’s disease (AD). The findings help illuminate the interpretation of the EEG’s duration of dynamical stationarity and proposes to be useful for distinguishing AD patients from control patients. This study supports the idea of a compensatory activation of the frontotemporal region of the brain in the early stages of Alzheimer’...

• Type: Conference
• Year: 2007
• Where: ICONIP
• Authors: Charles-Francois Vincent Latchoumane, Emmanuel C. Ifeachor, Nigel R. Hudson, Sunil Wimalaratna, Jaeseung Jeong
• Keywords: Alzheimer’s Disease, EEG Time Series, ICONIP 2007, Information Technology, Mean Stationarity Duration