Non-linear Analysis of the Electroencephalogram in Alzheimer’s Disease by Means of Symbolic Sequence Decomposition Method

Abstract

In this pilot study, a symbolic sequence decomposition method was used in conjunction with Shannon’s entropy to investigate the changes in electroencephalogram signals of 11 patients with Alzheimer’s disease and 11 age-matched control subjects. Results were statistically analysed by student t-test and later classified with receiver operating curves. Statistically significant differences between both groups were found at electrodes Fp1, O2, P3, T4 and T5. Sensitivity (defined as percentages of correctly classified patients) and specificity (defined as correctly classified controls) were evaluated using the receiver operating curves method. Accuracy of the methods was calculated according to sensitivity and specificity measures of electrodes showing statistically significant differences between the control group and Alzheimer’s disease patients and ranged between 72.73-77.27%. These accuracy values were in agreement with previously published entropy studies on this data set. Although combining these methods did not provide any greater accuracy over previous findings, using a symbolic sequence decomposition method enhanced the data processing.

Keywords

• Alzheimer’s Disease, Electroencephalogram, Non-linear Analysis, Symbolic Sequence Decomposition, Shannon’s Entropy

Non-linear Analysis of the Electroencephalogram in Alzheimer’s Disease by Means of Symbolic Sequence Decomposition Method

Öz

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