Unbiased estimation of permutation entropy in EEG analysis for Alzheimer's disease classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F18%3A10381611" target="_blank" >RIV/00216208:11150/18:10381611 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21340/18:00316543

Výsledek na webu

<a href="https://doi.org/10.1016/j.bspc.2017.08.012" target="_blank" >https://doi.org/10.1016/j.bspc.2017.08.012</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2017.08.012" target="_blank" >10.1016/j.bspc.2017.08.012</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unbiased estimation of permutation entropy in EEG analysis for Alzheimer's disease classification

Popis výsledku v původním jazyce

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&apos;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&apos;s disease, we observed a statistically significant decrease in permutation entropy at all channels.

Název v anglickém jazyce

Unbiased estimation of permutation entropy in EEG analysis for Alzheimer's disease classification

Popis výsledku anglicky

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&apos;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&apos;s disease, we observed a statistically significant decrease in permutation entropy at all channels.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

—

Svazek periodika

39

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

424-430

Kód UT WoS článku

000412607900039

EID výsledku v databázi Scopus

2-s2.0-85028325768