Classification of Alzheimer’s disease from Quadratic Sample Entropy of the EEG

Abstract

Currently accepted input parameter limitations in entropy based, non-linear signal processing methods, e.g. Sample Entropy (SampEn), may limit the information gathered from tested biological signals. This study investigates the ability of Quadratic Sample Entropy (QSE) to identify changes in electroencephalogram (EEG) signals of 11 patients with a diagnosis of Alzheimer’s Disease (AD) and 11 age-matched, healthy controls. QSE measures signal regularity, where reduced QSE values indicate greater regularity. This method allows a greater range of QSE input parameters to produce reliable results than SampEn. QSE was lower in AD patients compared to controls with significant differences (p <0.01) for different parameter combinations at electrodes P3, P4, O1 and O2. Subject and epoch-based classifications were tested with leave-one-out linear discriminant analysis. The maximum diagnostic accuracy and area under Receiver Operating Characteristic curve were 77.27% and over 80% respectively at many parameter and electrode combinations. Furthermore QSE results across all r values were consistent, suggesting QSE is robust for a wider range of input parameters than SampEn. The best results were obtained with input parameters outside the acceptable range for SampEn, and can identify EEG changes between AD patients and controls. However, caution should be applied due to the small sample size.

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Cite this paper

@inproceedings{Simons2015ClassificationOA, title={Classification of Alzheimer’s disease from Quadratic Sample Entropy of the EEG}, author={Stefaan J R Simons and Daniel Ab{\'a}solo}, year={2015} }