Unbiased estimation of permutation entropy in EEG analysis for Alzheimer's disease classification
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21340/18:00316543
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Unbiased estimation of permutation entropy in EEG analysis for Alzheimer's disease classification
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Biomedical Signal Processing and Control
ISSN
1746-8094
e-ISSN
—
Volume of the periodical
39
Issue of the periodical within the volume
January
Country of publishing house
GB - UNITED KINGDOM
Number of pages
7
Pages from-to
424-430
UT code for WoS article
000412607900039
EID of the result in the Scopus database
2-s2.0-85028325768