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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science 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)
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
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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