Deep Generative Networks for Algorithm Development in Implantable Neural Technology

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

Deep Generative Networks for Algorithm Development in Implantable Neural Technology

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

<a href="/en/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Quality internal grants at BUT</a><br>

Continuities

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

Publication year

2022

Confidentiality

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

Article name in the collection

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

ISBN

978-1-6654-5258-8

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

1736-1741

Publisher name

IEEE

Place of publication

Prague. Czechia

Event location

Praha

Event date

Oct 9, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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