Unbiased estimation of permutation entropy in EEG analysis for Alzheimer's disease classification. (January 2018) - Tylová, Lucie; Kukal, Jaromír; Hubata-Vacek, Václav; Vyšata, Oldřich
Graphical abstract: Highlights: The EEG signal of Alzheimer's diseased and healthy patients is studied via permutation entropy. The technique enables to use bigger window size and study influence of EEG sampling frequency. Statistically significant decreasing of permutation entropy is observed for Alzheimer's disease. Abstract: The EEG signal of healthy patient can be recognized as an output of a chaotic system. There are many measures of chaotic behaviour: Hurst and Lyapunov exponents, various dimensions of attractor, various entropy measures, etc. We prefer permutation entropy of equidistantly sampled data. The novelty of our approach is in bias reduction of permutation entropy estimates, memory decrease, and time complexities of permutation analysis. Therefore, we are not limited by the EEG signal and permutation sample lengths. This general method was used for channel by channel analysis of Alzheimer's diseased (AD) and healthy (CN) patients to point out the differences between AD and CN groups. Our technique also enables to study the influence of EEG sampling frequency in a wide range. The best results were obtained for sampling frequency 200 Hz, using at most window of length 10. In the case of Alzheimer's disease, we observed a statistically significant decrease in permutation entropy at all channels.