Multiscale Fluctuation-based Dispersion Entropy and its Applications to Neurological Diseases.

Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns. To quantify the physiological dynamics over multiple time scales, multiscale FDispEn (MEDE) is developed in this paper. MEDE is robust to the presence of baseline wanders or trends in the data. We evaluate MEDE, compared with popular multiscale sample entropy (MSE), multiscale fuzzy entropy (MEE), and the recently introduced multiscale dispersion entropy (MDE), on selected synthetic data and five neurological diseases' datasets: 1) focal and non-focal electroencephalograms (EEGs); 2) walking stride interval signals for young, elderly, and Parkinson's subjects; 3) stride interval fluctuations for Huntington's disease and amyotrophic lateral sclerosis; 4) EEGs for controls and Alzheimer's disease patients; and 5) eye movement data for Parkinson's disease and ataxia. The MEDE avoids the problem of the undefined MSE values and, compared with the MEE and MSE, leads to more stable entropy values over the scale factors for white and pink noises. Overall, the MEDE is the fastest and most consistent method for the discrimination of different states of neurological data, especially where the mean value of a time series considerably changes along with the signal (e.g., eye movement data). This paper shows that MFDE is a relevant new metric to gain further insights into the dynamics of neurological diseases' recordings. The MATLAB codes for the MEDE and its refined composite form are available in Xplore.