Dispersion Entropy for the Analysis of Resting-state MEG Regularity in Alzheimer’s Disease

Abstract:

Entropy as a fundamental notion in information theory assesses the dynamical characteristics of time series. To alleviate the problems of existing entropy methods, we have recently introduced dispersion entropy (DisEn) as a very fast and powerful tool to quantify the regularity of signals. Changes in entropy methods have been reported useful in research studies to characterize Alzheimer’s disease (AD) as a progressive degenerative brain disorder. The aim of this study is to evaluate the ability of DisEn, in comparison with fuzzy entropy (FuzEn), sample entropy (SampEn), and permutation entropy (PerEn), to discriminate 36 AD patients from 26 elderly control subjects using resting-state magnetoencephalogram signals. The results obtained by DisEn, FuzEn, and SampEn, unlike PerEn, show that the AD patients’ signals are more regular than controls’ time series. The adjusted p-values demonstrate the superiority of DisEn over PerEn, SampEn, and PerEn. Moreover, the computation time for the newly proposed DisEn method is noticeably less than that for the FuzEn, SampEn, and PerEn approaches.

Hamed Azami is a PhD student in The Institute for Digital Communications, University of Edinburgh, UK. His research interest is developing signal processing and pattern recognition techniques with major applications in biomedical data and neuroscience.