title: Classification of Alzheimer's disease from quadratic sample entropy of electroencephalogram.
abstract: Currently accepted input parameter limitations in entropy-based, non-linear signal processing methods, for example, sample entropy (SampEn), may limit the information gathered from tested biological signals. 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 is investigated. QSE measures signal regularity, where reduced QSE values indicate greater regularity. The presented method allows a greater range of QSE input parameters to produce reliable results than SampEn. QSE was lower in AD patients compared with 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 the receiver operating characteristic curve were 77.27 and more than 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 because of the small sample size.
authors.affiliation: Centre for Biological Engineering , Department of Mechanical Engineering Sciences , Faculty of Engineering and Physical Sciences (J5) , University of Surrey , Guildford , GU2 7XH , UK.
authors.forename: Samantha
authors.initials: S
authors.lastname: Simons
authors.affiliation: Centre for Biological Engineering , Department of Mechanical Engineering Sciences , Faculty of Engineering and Physical Sciences (J5) , University of Surrey , Guildford , GU2 7XH , UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Abasolo
authors.affiliation: Institute for Digital Communications , School of Engineering , The University of Edinburgh , Edinburgh , EH9 3JL , UK.
authors.forename: Javier
authors.initials: J
authors.lastname: Escudero
keywords: Alzheimer disease classification; EEG; O1 electrodes; O2 electrodes; P3 electrodes; P4 electrodes; QSE input parameters; biomedical electrodes; diseases; electroencephalogram; electroencephalography; entropy; epoch-based classifications; leave-one-out discriminant analysis; maximum diagnostic accuracy; medical signal processing; nonlinear signal processing methods; quadratic sample entropy; receiver operating characteristic curve; sensitivity analysis; signal classification; signal regularity; subject-based classifications; tested biological signals