Feasibility of seizure risk prediction using intracranial EEG measurements in dogs

Farid Yaghouby; Behrouz Madahian; Hossein Mirinejad; Sridhar Sunderam

doi:10.1109/NEBEC.2015.7117179

Feasibility of seizure risk prediction using intracranial EEG measurements in dogs

Farid Yaghouby, Behrouz Madahian, Hossein Mirinejad, Sridhar Sunderam

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Patients with refractory epilepsy would greatly benefit from an accurate seizure forecasting system. This paper introduces a seizure prediction algorithm based on a random forest classifier that uses features computed from continuous intracranial electroencephalographic (iEEG) measurements in dogs with naturally occurring epilepsy. Results suggest that the proposed model can distinguish between interictal (baseline) and preictal (pre-seizure) periods and provide an intuitive measure of seizure risk that may have practical utility.

Original language	English
Title of host publication	2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015
ISBN (Electronic)	9781479983605
DOIs	https://doi.org/10.1109/NEBEC.2015.7117179
State	Published - Jun 2 2015
Event	2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015 - Troy, United States Duration: Apr 17 2015 → Apr 19 2015

Publication series

Name	2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015

Conference

Conference	2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015
Country/Territory	United States
City	Troy
Period	4/17/15 → 4/19/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

EEG
Epilepsy
Random forest
Seizure prediction

ASJC Scopus subject areas

Biotechnology
Cancer Research
Cell Biology
Molecular Medicine
Biomedical Engineering
Control and Systems Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/NEBEC.2015.7117179

Cite this

@inproceedings{9d9b60748e4148dc82918d2be84f913e,

title = "Feasibility of seizure risk prediction using intracranial EEG measurements in dogs",

abstract = "Patients with refractory epilepsy would greatly benefit from an accurate seizure forecasting system. This paper introduces a seizure prediction algorithm based on a random forest classifier that uses features computed from continuous intracranial electroencephalographic (iEEG) measurements in dogs with naturally occurring epilepsy. Results suggest that the proposed model can distinguish between interictal (baseline) and preictal (pre-seizure) periods and provide an intuitive measure of seizure risk that may have practical utility.",

keywords = "EEG, Epilepsy, Random forest, Seizure prediction",

author = "Farid Yaghouby and Behrouz Madahian and Hossein Mirinejad and Sridhar Sunderam",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015 ; Conference date: 17-04-2015 Through 19-04-2015",

year = "2015",

month = jun,

day = "2",

doi = "10.1109/NEBEC.2015.7117179",

language = "English",

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booktitle = "2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015",

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Yaghouby, F, Madahian, B, Mirinejad, H & Sunderam, S 2015, Feasibility of seizure risk prediction using intracranial EEG measurements in dogs. in 2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015., 7117179, 2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015, 2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015, Troy, United States, 4/17/15. https://doi.org/10.1109/NEBEC.2015.7117179

Feasibility of seizure risk prediction using intracranial EEG measurements in dogs. / Yaghouby, Farid; Madahian, Behrouz; Mirinejad, Hossein et al.
2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015. 2015. 7117179 (2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Feasibility of seizure risk prediction using intracranial EEG measurements in dogs

AU - Yaghouby, Farid

AU - Madahian, Behrouz

AU - Mirinejad, Hossein

AU - Sunderam, Sridhar

PY - 2015/6/2

Y1 - 2015/6/2

N2 - Patients with refractory epilepsy would greatly benefit from an accurate seizure forecasting system. This paper introduces a seizure prediction algorithm based on a random forest classifier that uses features computed from continuous intracranial electroencephalographic (iEEG) measurements in dogs with naturally occurring epilepsy. Results suggest that the proposed model can distinguish between interictal (baseline) and preictal (pre-seizure) periods and provide an intuitive measure of seizure risk that may have practical utility.

AB - Patients with refractory epilepsy would greatly benefit from an accurate seizure forecasting system. This paper introduces a seizure prediction algorithm based on a random forest classifier that uses features computed from continuous intracranial electroencephalographic (iEEG) measurements in dogs with naturally occurring epilepsy. Results suggest that the proposed model can distinguish between interictal (baseline) and preictal (pre-seizure) periods and provide an intuitive measure of seizure risk that may have practical utility.

KW - EEG

KW - Epilepsy

KW - Random forest

KW - Seizure prediction

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M3 - Conference contribution

AN - SCOPUS:84941072499

T3 - 2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015

BT - 2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015

T2 - 2015 41st Annual Northeast Biomedical Engineering Conference, NEBEC 2015

Y2 - 17 April 2015 through 19 April 2015

ER -

Feasibility of seizure risk prediction using intracranial EEG measurements in dogs

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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