Optimal Parameters of Adaptive Segmentation for Epileptic Graphoelements Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F17%3A00311885" target="_blank" >RIV/68407700:21460/17:00311885 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/17:00311885

Výsledek na webu

<a href="https://www.radioeng.cz/fulltexts/2017/17_01_0323_0329.pdf" target="_blank" >https://www.radioeng.cz/fulltexts/2017/17_01_0323_0329.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/re.2017.0323" target="_blank" >10.13164/re.2017.0323</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimal Parameters of Adaptive Segmentation for Epileptic Graphoelements Recognition

Popis výsledku v původním jazyce

Manual review of EEG records, as it is performed in common medical practice, is very time-consuming. There is an effort to make this analysis easier and faster for neurologists by using systems for automatic EEG graphoelements recognition. Such a system is composed of three steps: (1) segmentation, which is a subject of this article, (2) features extraction and (3) classification. Precision of classification, and thereby the whole recognition, is strongly affected by the quality of preceding segmentation procedure, which depends on the method of segmentation and its parameters. In this paper, Varri's method for segmentation of real epileptic EEG signals is used. Effect of input parameters on segmentation outcome is discussed and parameters values are proposed to achieve optimal outcome suitable for the following classification and graphoelements recognition. Only the results of segmentation are presented in this paper.

Název v anglickém jazyce

Optimal Parameters of Adaptive Segmentation for Epileptic Graphoelements Recognition

Popis výsledku anglicky

Manual review of EEG records, as it is performed in common medical practice, is very time-consuming. There is an effort to make this analysis easier and faster for neurologists by using systems for automatic EEG graphoelements recognition. Such a system is composed of three steps: (1) segmentation, which is a subject of this article, (2) features extraction and (3) classification. Precision of classification, and thereby the whole recognition, is strongly affected by the quality of preceding segmentation procedure, which depends on the method of segmentation and its parameters. In this paper, Varri's method for segmentation of real epileptic EEG signals is used. Effect of input parameters on segmentation outcome is discussed and parameters values are proposed to achieve optimal outcome suitable for the following classification and graphoelements recognition. Only the results of segmentation are presented in this paper.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Radioengineering

ISSN

1210-2512

e-ISSN

—

Svazek periodika

26

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

7

Strana od-do

323-329

Kód UT WoS článku

000399735900042

EID výsledku v databázi Scopus

2-s2.0-85018337281