AUTOMATIC EEG CLASSIFICATION USING DENSITY BASED ALGORITHMS DBSCAN AND DENCLUE

Authors

  • Marek Piorecký Czech Technical University in Prague, Faculty of Biomedical Engineering, National Institute of Mental Health
  • Jan Štrobl Czech Technical University in Prague, Faculty of Biomedical Engineering, National Institute of Mental Health
  • Vladimír Krajča Czech Technical University in Prague, Faculty of Biomedical Engineering, National Institute of Mental Health

DOI:

https://doi.org/10.14311/AP.2019.59.0498

Keywords:

EEG, DBSCAN, DENCLUE, automatic classification, epilepsy.

Abstract

Electroencephalograph (EEG) is a commonly used method in neurological practice. Automatic classifiers (algorithms) highlight signal sections with interesting activity and assist an expert with record scoring. Algorithm K-means is one of the most commonly used methods for EEG inspection. In this paper, we propose/apply a method based on density-oriented algorithms DBSCAN and DENCLUE. DBSCAN and DENCLUE separate the nested clusters against K-means. All three algorithms were validated on a testing dataset and after that adapted for a real EEG records classification. 24 dimensions EEG feature space were classified into 5 classes (physiological, epileptic, EOG, electrode, and EMG artefact). Modified DBSCAN and DENCLUE create more than two homogeneous classes of the epileptic EEG data. The results offer an opportunity for the EEG scoring in clinical practice. The big advantage of the proposed algorithms is the high homogeneity of the epileptic class.

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Published

2019-11-01

Issue

Vol. 59 No. 5 (2019)

Section

Articles

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How to Cite

Piorecký, M., Štrobl, J., & Krajča, V. (2019). AUTOMATIC EEG CLASSIFICATION USING DENSITY BASED ALGORITHMS DBSCAN AND DENCLUE. Acta Polytechnica, 59(5), 498-509. https://doi.org/10.14311/AP.2019.59.0498