Pattern Discovery in an EEG Database of Depression Patients: Preliminary Results

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F23%3A43921226" target="_blank" >RIV/00023752:_____/23:43921226 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10164584" target="_blank" >https://ieeexplore.ieee.org/document/10164584</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/MEASUREMENT59122.2023.10164584" target="_blank" >10.23919/MEASUREMENT59122.2023.10164584</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Pattern Discovery in an EEG Database of Depression Patients: Preliminary Results

Popis výsledku v původním jazyce

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient&apos;s brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project &quot;Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants&quot;ongoing at the University of Vienna, the Czech Academy of Sciences and the National Institute of Mental Health in the Czech Republic.

Název v anglickém jazyce

Pattern Discovery in an EEG Database of Depression Patients: Preliminary Results

Popis výsledku anglicky

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient&apos;s brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project &quot;Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants&quot;ongoing at the University of Vienna, the Czech Academy of Sciences and the National Institute of Mental Health in the Czech Republic.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 14th International Conference on Measurement, MEASUREMENT 2023

ISBN

978-80-972629-7-6

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

80-83

Název nakladatele

Institute of Measurement Science, SAS

Místo vydání

Karlova Ves, Slovensko

Místo konání akce

Smolenice Castle

Datum konání akce

29. 5. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—