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Classification of Alzheimer's disease from quadratic sample entropy of electroencephalogram

Simons, S, Abasolo, D and Escudero, J (2015) Classification of Alzheimer's disease from quadratic sample entropy of electroencephalogram IET Healthcare Technology Letters, 2 (3). pp. 70-73.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
AuthorsEmailORCID
Simons, SUNSPECIFIEDUNSPECIFIED
Abasolo, DUNSPECIFIEDUNSPECIFIED
Escudero, JUNSPECIFIEDUNSPECIFIED
Date : 1 May 2015
Identification Number : 10.1049/htl.2014.0106
Additional Information : © The Institution of Engineering and Technology. This paper is a postprint of a paper submitted to and accepted for publication in IET Healthcare Technology Letters and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.
Depositing User : Symplectic Elements
Date Deposited : 16 Sep 2015 16:46
Last Modified : 16 Sep 2015 16:46
URI: http://epubs.surrey.ac.uk/id/eprint/807594

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