Open AccessClassification of Alzheimer's disease from quadratic sample entropy of electroencephalogram
Author(s) -
Simons Samantha,
Abasolo Daniel,
Escudero Javier
Publication year - 2015
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2014.0106
Subject(s) - sample entropy , discriminant , entropy (arrow of time) , receiver operating characteristic , linear discriminant analysis , quadratic equation , mathematics , statistics , range (aeronautics) , pattern recognition (psychology) , artificial intelligence , computer science , biology , time series , physics , materials science , geometry , quantum mechanics , composite material
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.