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Automatic eeg classification using density based algorithms DBSCAN AND DENCLUE

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F19%3A43920044" target="_blank" >RIV/00023752:_____/19:43920044 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21460/19:00335131

  • Result on the web

    <a href="https://ojs.cvut.cz/ojs/index.php/ap/article/view/5377" target="_blank" >https://ojs.cvut.cz/ojs/index.php/ap/article/view/5377</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.14311/AP.2019.59.0498" target="_blank" >10.14311/AP.2019.59.0498</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automatic eeg classification using density based algorithms DBSCAN AND DENCLUE

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    <a href="/en/project/GA17-20480S" target="_blank" >GA17-20480S: Temporal context in analysis of long-term non-stationary multidimensional signal</a><br>

  • Continuities

    V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Acta Polytechnica

  • ISSN

    1210-2709

  • e-ISSN

  • Volume of the periodical

    59

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    12

  • Pages from-to

    498-509

  • UT code for WoS article

    000494638900005

  • EID of the result in the Scopus database

    2-s2.0-85077881634