Sleep Spindles Detection Using Empirical Mode Decomposition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00237334" target="_blank" >RIV/68407700:21230/15:00237334 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/15:00237334 RIV/00023752:_____/15:43914841

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7347063" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&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

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

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

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

České vysoké učení technické v Praze

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

29. 10. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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