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

0573674 - ÚI 2024 RIV SK eng C - Konferenční příspěvek (zahraniční konf.)
Hlaváčková-Schindler, Kateřina - Pacher, C. - Plant, C. - Lazarenko, M. - Paluš, Milan - Hlinka, Jaroslav - Kathpalia, Aditi - Brunovský, M.
Pattern Discovery in an EEG Database of Depression Patients: Preliminary Results.
2023 14th International Conference on Measurement. Proceedings. Bratislava: Institute of Measurement Science, SAS / IEEE, 2023 - (Dvurečenskij, A.; Maňka, J.; Švehlíková, J.; Witkovský, V.), s. 80-83. ISBN 979-8-3503-1218-8.
[MEASUREMENT 2023: International Conference on Measurement /14./. Smolenice (SK), 29.05.2023-31.05.2023]
Grant CEP: GA ČR(CZ) GF21-14727K
Institucionální podpora: RVO:67985807
Klíčová slova: Major Depressive Disorder * Interactive Clustering * Granger Causality * Classification Methods
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://dx.doi.org/10.23919/MEASUREMENT59122.2023.10164584

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.
Trvalý link: https://hdl.handle.net/11104/0344055