Methods for automatic estimation of the number of clusters for K-means algorithm used on eeg signal: Feasibility study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F17%3A43919235" target="_blank" >RIV/00023752:_____/17:43919235 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/17:00316710

Result on the web

<a href="https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/4474/4427" target="_blank" >https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/4474/4427</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Methods for automatic estimation of the number of clusters for K-means algorithm used on eeg signal: Feasibility study

Original language description

Lots of brain diseases are recognized by EEG recording. EEG signal has a stochastic character, this stochastic nature makes the evaluation of EEG recording complicated. Therefore we use automatic classification methods for EEG processing. These methods help the expert to find significant or physiologically important segments in the EEG recording. The k-means algorithm is a frequently used method in practice for automatic classification. The main disadvantage of the k-means algorithm is the necessary determination of the number of clusters. So far there are many methods which try to determine optimal number of clusters for k-means algorithm. The aim of this study is to test functionality of the two most frequently used methods on EEG signals, concretely the elbow and the silhouette method. In this feasibility study we compared the results of both methods on simulated data and real EEG signal. We want to prove with the help of an expert the possibility to use these functions on real EEG signal. The results show that the silhouette method applied on EEG recordings is more time-consuming than the elbow method. Neither of the methods is able to correctly recognize the number of clusters in the EEG record by expert evaluation and therefore it is not applicable to the automatic classification of EEG based on k-means algorithm.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2017

Confidentiality

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

Name of the periodical

Lékař a technika

ISSN

0301-5491

e-ISSN

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Volume of the periodical

47

Issue of the periodical within the volume

3

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

7

Pages from-to

81-87

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85038838297