Deep Generative Networks for Algorithm Development in Implantable Neural Technology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146334" target="_blank" >RIV/00216305:26220/22:PU146334 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21460/22:00362829 RIV/68407700:21730/22:00362829

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9945379" target="_blank" >https://ieeexplore.ieee.org/document/9945379</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SMC53654.2022.9945379" target="_blank" >10.1109/SMC53654.2022.9945379</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Generative Networks for Algorithm Development in Implantable Neural Technology

Popis výsledku v původním jazyce

Electrical stimulation of deep brain structures is an established therapy for drug-resistant focal epilepsy. The emerging implantable neural sensing and stimulating (INSS) technology enables simultaneous delivery of chronic deep brain stimulation (DBS) and recording of electrical brain activity from deep brain structures while patients live in their home environment. Long-term intracranial electroencephalography (iEEG) iEEG signals recorded by INSS devices represent an opportunity to investigate brain neurophysiology and how DBS affects neural circuits. However, novel algorithms and data processing pipelines need to be developed to facilitate research of these long-term iEEG signals. Early-stage analytical infrastructure development for INSS applications can be limited by lacking iEEG data that might not always be available. Here, we investigate the feasibility of utilizing the Deep Generative Adversarial Network (DCGAN) for synthetic iEEG data generation. We trained DCGAN using 3-second iEEG segments and validated synthetic iEEG usability by training a classification model, using synthetic iEEG only and providing a good classification performance on unseen real iEEG with an F1 score 0.849. Subsequently, we demonstrated the feasibility of utilizing the synthetic iEEG in the INSS application development by training a deep learning network for DBS artifact removal using synthetic data only and demonstrated the performance on real iEEG signals. The presented strategy of on-demand generating synthetic iEEG will benefit early-stage algorithm development for INSS applications.

Název v anglickém jazyce

Deep Generative Networks for Algorithm Development in Implantable Neural Technology

Popis výsledku anglicky

Electrical stimulation of deep brain structures is an established therapy for drug-resistant focal epilepsy. The emerging implantable neural sensing and stimulating (INSS) technology enables simultaneous delivery of chronic deep brain stimulation (DBS) and recording of electrical brain activity from deep brain structures while patients live in their home environment. Long-term intracranial electroencephalography (iEEG) iEEG signals recorded by INSS devices represent an opportunity to investigate brain neurophysiology and how DBS affects neural circuits. However, novel algorithms and data processing pipelines need to be developed to facilitate research of these long-term iEEG signals. Early-stage analytical infrastructure development for INSS applications can be limited by lacking iEEG data that might not always be available. Here, we investigate the feasibility of utilizing the Deep Generative Adversarial Network (DCGAN) for synthetic iEEG data generation. We trained DCGAN using 3-second iEEG segments and validated synthetic iEEG usability by training a classification model, using synthetic iEEG only and providing a good classification performance on unseen real iEEG with an F1 score 0.849. Subsequently, we demonstrated the feasibility of utilizing the synthetic iEEG in the INSS application development by training a deep learning network for DBS artifact removal using synthetic data only and demonstrated the performance on real iEEG signals. The presented strategy of on-demand generating synthetic iEEG will benefit early-stage algorithm development for INSS applications.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Kvalitní interní granty VUT</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

ISBN

978-1-6654-5258-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1736-1741

Název nakladatele

IEEE

Místo vydání

Prague. Czechia

Místo konání akce

Praha

Datum konání akce

9. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—