Automatic eeg classification using density based algorithms DBSCAN AND DENCLUE

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21460/19:00335131

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic eeg classification using density based algorithms DBSCAN AND DENCLUE

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automatic eeg classification using density based algorithms DBSCAN AND DENCLUE

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA17-20480S" target="_blank" >GA17-20480S: Časový kontext v úloze analýzy dlouhodobého nestacionárního vícerozměrného signálu</a><br>

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Acta Polytechnica

ISSN

1210-2709

e-ISSN

—

Svazek periodika

59

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

12

Strana od-do

498-509

Kód UT WoS článku

000494638900005

EID výsledku v databázi Scopus

2-s2.0-85077881634