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Prediction of Stimulation Efficacy in Epilepsy (PRESEnCE)

Project goals

Proposed project is focused on the prediction of neurostimulation methods (vagal nerve stimulation, deep brain stimulation) efficacy in patients with epilepsy. Neurostimulation leads to significant seizure reduction in approximately 50% of implanted patient. By now, we are not able to identify patients who can benefit from implantation of neurostimulator pre-operatively. We managed to develop a statistic mode, which is able to predict efficacy of vagal nerve stimulation based on routine pre-implantation EEG. This statistic model predict efficacy of vagal nerve stimulation with high sensitivity and specificity. Proposed project is based on this statistic model, we would like to develop similar model for children with implanted vagal nerve stimulator and for patients with deep brain stimulation. At the same time, we realize the necessity to verify our statistic model on independent external data set, which we supposed to be crucial for the clinical application. The project is focused on the improvement of medical care in patients with drug-resistant epilepsy.

Keywords

epilepsieepilepsyneurostimulaceNeurostimulationhluboká mozková stimulacedeep brain stimulationstimulace vagového nervupredikce efektustatistický modelvagal nerve stimulationefficacy predictionstatistic model

Public support

  • Provider

    Ministry of Health

  • Programme

    Programme to support medical applied research in 2015 to 2022

  • Call for proposals

    Zdravotnický AV 5 (SMZ0201900001)

  • Main participants

    Fakultní nemocnice u sv. Anny v Brně

  • Contest type

    VS - Public tender

  • Contract ID

    NV19-04-00343

Alternative language

  • Project name in Czech

    Predikce Efektu Stimulace u pacientů s Epilepsií (PRESEnCE)

  • Annotation in Czech

    Navrhovaný projekt se zaměřuje na predikci efektu neurostimulačních metod (vagové stimulace, hluboké mozkové stimulace) u pacientů s epilepsií. V současnosti je známo, že neurostimulační metody vedou k významné redukci záchvatů cca u 50% implantovaných pacientů. Dosud však nejsme schopni pacienty, kteří profitují z implantace neurostimulátoru, identifikovat na základě jejich předimplantačních dat. Našemu kolektivu se podařilo vyvinout statistický model, který predikuje efekt vagové stimulace na základě rutinního EEG, které bylo natočeno před implantací. Tento statistický model predikuje efekt vagové stimulace s vysokou sensitivitou a specificitou. Nynější projekt vychází z tohoto statistického modelu, snahou je vytvořit obdobný model pro děti s implantovaný vagovým stimulátorem a pro pacienty s hlubokou mozkovou stimulací. Současně si rovněž uvědomujeme nutnost otestovat statistický model na externích datech, toto testování vnímáme jako zásadní pro jeho klinické využití. Projekt se zaměřuje na zlepšení péče o pacienty s farmakorezistentní epilepsií.

Scientific branches

  • R&D category

    AP - Applied research

  • OECD FORD - main branch

    30210 - Clinical neurology

  • OECD FORD - secondary branch

  • OECD FORD - another secondary branch

  • FH - Neurology, neuro-surgery, nuero-sciences

Completed project evaluation

  • Provider evaluation

    V - Vynikající výsledky projektu (s mezinárodním významem atd.)

  • Project results evaluation

    Overall, I consider it a very high-quality project that provided new information and publications and I consider it to be well-executed, so I recommend a positive evaluation. Prediction of vagal stimulation results based on EEG data routinely obtained during pre-implantation examination. Elaboration of the design of a large-scale clinical study, which is made possible by the fact that the model is applicable to various EEG systems - confirmed by an accepted publication. The possibility of use also in pediatric patients (certain limitation due to difficult to solve hyperventilation). Perspective for use to predict the effect of deep brain stimulation of the anterior thalamic nucleus in patients with pharmacoresistant epilepsy. The basis for further work on EEG signal processing using other techniques – methods for estimating signal complexity, which are part of the concept of information theory. Currently, supervised deep learning techniques have been proven to be an effective tool for

Solution timeline

  • Realization period - beginning

    May 1, 2019

  • Realization period - end

    Dec 31, 2023

  • Project status

    U - Finished project

  • Latest support payment

    Apr 19, 2022

Data delivery to CEP

  • Confidentiality

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

  • Data delivery code

    CEP24-MZ0-NV-U

  • Data delivery date

    Aug 1, 2024

Finance

  • Total approved costs

    8,856 thou. CZK

  • Public financial support

    8,856 thou. CZK

  • Other public sources

    0 thou. CZK

  • Non public and foreign sources

    0 thou. CZK