Deep-learning for seizure forecasting in canines with epilepsy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F19%3A00508712" target="_blank" >RIV/68081731:_____/19:00508712 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/19:00331606 RIV/00159816:_____/19:00071067

Result on the web

<a href="https://iopscience.iop.org/article/10.1088/1741-2552/ab172d" target="_blank" >https://iopscience.iop.org/article/10.1088/1741-2552/ab172d</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1741-2552/ab172d" target="_blank" >10.1088/1741-2552/ab172d</a>

Result language

angličtina

Original language name

Deep-learning for seizure forecasting in canines with epilepsy

Original language description

Objective. This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy Assist Device) in a pseudo-prospective mode using iEEG from four canines with naturally occurring epilepsy. Approach. The system was trained and tested on 75 seizures collected over 1608 d utilizing a genetic algorithm to optimize forecasting hyper-parameters (prediction horizon (PH), median filter window length, and probability threshold) for each subject-specific seizure forecasting model. The trained CNN models were deployed on a hand-held tablet computer and tested on testing iEEG datasets from four canines. The results from the iEEG testing datasets were compared with Monte Carlo simulations using a Poisson random predictor with equal time in warning to evaluate seizure forecasting performance. Main results. The results show the CNN models forecasted seizures at rates significantly above chance in all four dogs (p < 0.01, with mean 0.79 sensitivity and 18% time in warning). The deep learning method presented here surpassed the performance of previously reported methods using computationally expensive features with standard machine learning methods like logistic regression and support vector machine classifiers. Significance. Our findings principally support the feasibility of deploying trained CNN models on a hand-held computational device (Mayo Epilepsy Assist Device) that analyzes streaming iEEG data for real-time seizure forecasting.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20602 - Medical laboratory technology (including laboratory samples analysis; diagnostic technologies) (Biomaterials to be 2.9 [physical characteristics of living material as related to medical implants, devices, sensors])

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2019

Confidentiality

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

Name of the periodical

Journal of Neural Engineering

ISSN

1741-2560

e-ISSN

—

Volume of the periodical

16

Issue of the periodical within the volume

3

Country of publishing house

GB - UNITED KINGDOM

Number of pages

10

Pages from-to

036031

UT code for WoS article

000467476200005

EID of the result in the Scopus database

2-s2.0-85065805741