An Incremental Approach to Clinical EEG Data Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00219615" target="_blank" >RIV/68407700:21230/15:00219615 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21460/15:00219615

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-11128-5_122" target="_blank" >http://dx.doi.org/10.1007/978-3-319-11128-5_122</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-11128-5_122" target="_blank" >10.1007/978-3-319-11128-5_122</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Incremental Approach to Clinical EEG Data Classification

Popis výsledku v původním jazyce

Experimental verification of two approaches of incremental learning to clinical EEG data classification is presented. Three datasets were used to evaluate their performance. In particular sleep, artifact and newborn EEG signals. Most accurate classifiersthat are able to detect unwanted elements in EEG signal are found using artifact data set and tested on sleep and newborn datasets. Classification is performed using two incremental classifiers: Support Vector Machine and 1-Nearest Neighbors. The classifiers are able to classify sleep (10%) and newborn (0.5%) datasets by learning the shortest part of the data set with sufficient accuracy at least 70%. Better and quicker results were obtained by random rather than sequential selection of data. In conclusion it means that the classification is made fast. The incremental approach is supposed to save time, which neurologists spend during manual EEG signal scoring.

Název v anglickém jazyce

An Incremental Approach to Clinical EEG Data Classification

Popis výsledku anglicky

Experimental verification of two approaches of incremental learning to clinical EEG data classification is presented. Three datasets were used to evaluate their performance. In particular sleep, artifact and newborn EEG signals. Most accurate classifiersthat are able to detect unwanted elements in EEG signal are found using artifact data set and tested on sleep and newborn datasets. Classification is performed using two incremental classifiers: Support Vector Machine and 1-Nearest Neighbors. The classifiers are able to classify sleep (10%) and newborn (0.5%) datasets by learning the shortest part of the data set with sufficient accuracy at least 70%. Better and quicker results were obtained by random rather than sequential selection of data. In conclusion it means that the classification is made fast. The incremental approach is supposed to save time, which neurologists spend during manual EEG signal scoring.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

<a href="/cs/project/EE2.3.30.0034" target="_blank" >EE2.3.30.0034: Podpora zkvalitnění týmů výzkumu a vývoje a rozvoj intersektorální mobility na ČVUT v Praze</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

IFMBE Proceedings - 6th European Conference of the International Federation for Medical and Biological Engineering

ISBN

978-3-319-11127-8

ISSN

1680-0737

e-ISSN

—

Počet stran výsledku

4

Strana od-do

489-492

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Dubrovník

Datum konání akce

7. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000349454200122