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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&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.

  • 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