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access icon openaccess Classification of Alzheimer's disease from quadratic sample entropy of electroencephalogram

  • Author(s): Samantha Simons 1 ; Daniel Abasolo 1 ; Javier Escudero 2
    • View affiliations
    • Affiliations: 1: Centre for Biological Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences (J5), University of Surrey, Guildford, GU2 7XH, UK ;
      2: Institute for Digital Communications, School of Engineering, The University of Edinburgh, Edinburgh, EH9 3JL, UK
  • Source: Volume 2, Issue 3, June 2015, p. 70 – 73
    DOI:  10.1049/htl.2014.0106 , Online ISSN 2053-3713
© The Institution of Engineering and Technology
Received 19/12/2014, Accepted 06/03/2015, Revised 19/02/2015, Published 01/05/2015

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.

Inspec keywords: medical signal processing; electroencephalography; diseases; biomedical electrodes; signal classification; sensitivity analysis; entropy

Other keywords: leave-one-out discriminant analysis; quadratic sample entropy; receiver operating characteristic curve; maximum diagnostic accuracy; Alzheimer disease classification; P4 electrodes; subject-based classifications; O1 electrodes; electroencephalogram; signal regularity; tested biological signals; P3 electrodes; EEG; QSE input parameters; nonlinear signal processing methods; O2 electrodes; epoch-based classifications

Subjects: Biology and medical computing; Electrical activity in neurophysiological processes; Electrodiagnostics and other electrical measurement techniques; Signal processing and detection; Digital signal processing; Bioelectric signals

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