Pattern Discovery in an EEG Database of Depression Patients: Preliminary Results
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00573674" target="_blank" >RIV/67985807:_____/23:00573674 - isvavai.cz</a>
Result on the web
<a href="https://dx.doi.org/10.23919/MEASUREMENT59122.2023.10164584" target="_blank" >https://dx.doi.org/10.23919/MEASUREMENT59122.2023.10164584</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/MEASUREMENT59122.2023.10164584" target="_blank" >10.23919/MEASUREMENT59122.2023.10164584</a>
Alternative languages
Result language
angličtina
Original language name
Pattern Discovery in an EEG Database of Depression Patients: Preliminary Results
Original language description
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’s brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project 'Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants' ongoing at the University of Vienna, the Czech Academy of Sciences and the National Institute of Mental Health in the Czech Republic.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
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/GF21-14727K" target="_blank" >GF21-14727K: Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 14th International Conference on Measurement. Proceedings
ISBN
979-8-3503-1218-8
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
80-83
Publisher name
Institute of Measurement Science, SAS / IEEE
Place of publication
Bratislava
Event location
Smolenice
Event date
May 29, 2023
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
—