EEG analysis based on chaotic evaluation of variability

Abstract

-Electroencephalogram (EEG) analysis remains problematic due to both lack of understanding of the origins of the signal and inadequate evaluation methods. In spite of these shortcomings, the EEG is a valuable tool in the evaluation of some neurological disorders as well as in the evaluation of overall cerebral activity. It becomes more useful when combined with other clinical parameters. The focus of the work described here is twofold. New chaotic methods are introduced for EEG evaluation coupled with a hybrid system approach that permits the combination of the EEG results with clinical parameters to form a comprehensive decision model. The system is illustrated in an application for diagnosis of dementia. Extensions can easily be made to applications such as evaluation of brain activity during surgery.

Analysis of EEG background activity in Alzheimer's disease patients with Lempel–Ziv complexity and central tendency measure

“…We have not found relevant differences between the degree of variability from scatter plots of first differences of the EEG time series from both groups. However, our findings differ from other studies -with very small sample sizes -showing that the combination of CTM analysis of the EEG, clinical parameters and neuropsychological testing can be useful in the diagnosis of dementia [28,29]. Thus, the possible usefulness of CTM in the diagnosis of AD should be investigated with a larger number of patients and control subjects.…”

“…Preliminary studies indicate that the method can be adapted to determine the clinical significance of the variability findings in more complex time series such as the EEG [27]. Moreover, the combination of CTM analysis of the EEG, clinical parameters and neuropsychological testing can be useful in the diagnosis of AD [28] and in the differentiation among types of dementia [29].…”

Diagnostic Implications of EEG Analysis in Patients with Dementia

“…Recently, new methods of analysis have resulted in renewed interest in the EEG for diagnostic assistance in a number of diseases [3]. These new methods fall into a number of categories, including pre-processing algorithms [4], nonlinear signal analysis [5], and comparative analysis of electrode activity [6]. These techniques have shown promise in differentiation among types of dementia [7].…”