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METHODS FOR AUTOMATIC ESTIMATION OF THE NUMBER OF CLUSTERS FOR K-MEANS ALGORITHM USED ON EEG SIGNAL: FEASIBILITY STUDY

Autoři: Jan Štrobl1,2, Marek Piorecký1,2, Vladimír Krajča1

Autoři - působiště: 1Faculty of Biomedical Engineering, Czech Technical University in Prague Kladno, Czech Republic, 2National Institute of Mental Health, Klecany, Czech Republic

Článek: Lékař a technika - Clinician and Technology No. 3, 2017, 47, 81-87
Kategorie: Original research
Počet zobrazení článku: 21x

Specializace: biomedicína
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METHODS FOR AUTOMATIC ESTIMATION OF THE NUMBER OF CLUSTERS FOR K-MEANS ALGORITHM USED ON EEG SIGNAL: FEASIBILITY STUDY

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Summary

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.

Keywords:
silhouette, elbow method, EEG, k-means, automatic determination of number of clusters

 

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