Alzheimer disease diagnostics from EEG via Wishart distribution of fractional processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F21%3A00353487" target="_blank" >RIV/68407700:21340/21:00353487 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s11760-021-01875-9" target="_blank" >https://doi.org/10.1007/s11760-021-01875-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11760-021-01875-9" target="_blank" >10.1007/s11760-021-01875-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Alzheimer disease diagnostics from EEG via Wishart distribution of fractional processes

Popis výsledku v původním jazyce

Exact estimation of Hurst exponent from a signal is a complex task that determines the fractional character of the investigated sample. In this work, we propose a maximum likelihood technique using Wishart distribution and autocorrelation structure of the investigated time series. Unlike conventional methods, we perform signal segmentation and use the aggregated data to obtain an unbiased estimate of Hurst exponent. The efficiency of the estimation is validated by four different methods of fractional Brownian motion generation. The resulting estimates have very tiny confidence intervals as well as small mean square error. Additionally, the proposed methodology has been applied to 19-channel EEG time series and their Hurst exponent estimation related to the diagnostics of Alzheimer's disease.

Název v anglickém jazyce

Alzheimer disease diagnostics from EEG via Wishart distribution of fractional processes

Popis výsledku anglicky

Exact estimation of Hurst exponent from a signal is a complex task that determines the fractional character of the investigated sample. In this work, we propose a maximum likelihood technique using Wishart distribution and autocorrelation structure of the investigated time series. Unlike conventional methods, we perform signal segmentation and use the aggregated data to obtain an unbiased estimate of Hurst exponent. The efficiency of the estimation is validated by four different methods of fractional Brownian motion generation. The resulting estimates have very tiny confidence intervals as well as small mean square error. Additionally, the proposed methodology has been applied to 19-channel EEG time series and their Hurst exponent estimation related to the diagnostics of Alzheimer's disease.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

SIGNAL IMAGE AND VIDEO PROCESSING

ISSN

1863-1703

e-ISSN

1863-1711

Svazek periodika

15

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

1435-1442

Kód UT WoS článku

000627659700001

EID výsledku v databázi Scopus

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