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@proceedings{1674066,
author = {Doležalová, Irena and Chladek, J. and Koriťáková, Eva and Chrastina, Jan and Brázdil, Milan},
keywords = {vagal nerve stimulation efficacy; prediction},
language = {eng},
title = {Prediction of vagal nerve stimulation efficacy - validation of statistic model on external data set, pilot study},
url = {https://www.ean.org/fileadmin/user_upload/ean/congress-2020/Present/Abstracts/00_EAN_Journal_2020_Book.pdf},
year = {2020}
}
TY - CONF
ID - 1674066
AU - Doležalová, Irena - Chladek, J. - Koriťáková, Eva - Chrastina, Jan - Brázdil, Milan
PY - 2020
TI - Prediction of vagal nerve stimulation efficacy - validation of statistic model on external data set, pilot study
KW - vagal nerve stimulation efficacy
KW - prediction
UR - https://www.ean.org/fileadmin/user_upload/ean/congress-2020/Present/Abstracts/00_EAN_Journal_2020_Book.pdf
L2 - https://www.ean.org/fileadmin/user_upload/ean/congress-2020/Present/Abstracts/00_EAN_Journal_2020_Book.pdf
N2 - Vagal nerve stimulation (VNS) offers a possibility for a substantial seizure reduction in approximately 50% of implanted patients. However, there is a large group of patients who do not profit significantly from this therapy. At the moment, there is no widelyaccepted method for prediction of VNS efficacy based on pre-implantation data. Our group has developed and published a statistic classifier based on pre-implantation routine EEG, which was able to predict VNS response in a given patient with high accuracy. The crucial limitation of our previous work was its monocentric nature and the use of only one type of EEG recording system.
ER -
DOLEŽALOVÁ, Irena, J. CHLADEK, Eva KORIŤÁKOVÁ, Jan CHRASTINA and Milan BRÁZDIL. \textit{Prediction of vagal nerve stimulation efficacy - validation of statistic model on external data set, pilot study}. 2020. ISSN~1351-5101.
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