Sleep spindles detection using empirical mode decomposition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F15%3A43914841" target="_blank" >RIV/00023752:_____/15:43914841 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/15:00237334 RIV/68407700:21730/15:00237334

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7347063" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7347063</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IWCIM.2015.7347063" target="_blank" >10.1109/IWCIM.2015.7347063</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sleep spindles detection using empirical mode decomposition

Popis výsledku v původním jazyce

Sleep spindles are very important EEG patterns in modern neuroscience. There were developed many spindle detection algorithms, but not all of them are suitable for patients with insomnia because of artifacts, movements and complicated spindle producing. The paper presents a spindle detection method based on proper preprocessing and classification of stationary segments using Naive Bayes classifier. Preprocessing was performed using Empirical Mode Decomposition, which decomposes the signal into trends. Trends rejecting from the signal gives filtered signal for feature processing. To evaluate the quality of proposed approach, F-measure, positive predicative value and true positive rating were calculated. The method shows good results on dataset of 11 insomniac patient: F-measure by sample was 40.72% and F-measure by events was 48.59%. The results were also compared with Martin, Molle, Wendt and Ferallelli methods.

Název v anglickém jazyce

Sleep spindles detection using empirical mode decomposition

Popis výsledku anglicky

Sleep spindles are very important EEG patterns in modern neuroscience. There were developed many spindle detection algorithms, but not all of them are suitable for patients with insomnia because of artifacts, movements and complicated spindle producing. The paper presents a spindle detection method based on proper preprocessing and classification of stationary segments using Naive Bayes classifier. Preprocessing was performed using Empirical Mode Decomposition, which decomposes the signal into trends. Trends rejecting from the signal gives filtered signal for feature processing. To evaluate the quality of proposed approach, F-measure, positive predicative value and true positive rating were calculated. The method shows good results on dataset of 11 insomniac patient: F-measure by sample was 40.72% and F-measure by events was 48.59%. The results were also compared with Martin, Molle, Wendt and Ferallelli methods.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

International Workshop on Computational Intelligence for Multimedia Understanding

ISBN

978-1-4673-8457-5

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Prague, Czech Republic

Datum konání akce

29. 10. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—