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

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

Content Provider	IET Digital Library
Author	Simons, Samantha Abasolo, Daniel Escudero, Javier
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.
Starting Page	70
Ending Page	73
Page Count	4
Volume Number	2
e-ISSN	20533713
Issue Number	Issue 3, Jun (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/htl/2/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/htl.2014.0106
Journal	Healthcare Technology Letters
Publisher Date	2015-05-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Alzheimer Disease Classification Bioelectric Signal Biology And Medical Computing Biomedical Electrodes Digital Signal Processing Diseases EEG Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalogram Electroencephalography Entropy Epoch-based Classifications Leave-one-out Discriminant Analysis Maximum Diagnostic Accuracy Medical Signal Processing Nonlinear Signal Processing Method O1 Electrodes O2 Electrodes P3 Electrodes P4 Electrodes QSE Input Parameter Quadratic Sample Entropy Receiver Operating Characteristic Curve Sensitivity Analysis Signal Classification Signal Processing And Detection Signal Regularity Subject-based Classifications Tested Biological Signal
Content Type	Text
Resource Type	Article

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