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

Healthcare Technology Letters

article

Samantha Simons¹  Daniel Abasolo¹  Javier Escudero² 

[1] Centre for Biological Engineering, Department of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences (J5), University of Surrey;Institute for Digital Communications, School of Engineering, The University of Edinburgh

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

DOI  :  10.1049/htl.2014.0106

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

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