Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks: Algorithm Development and Optimization

Tal Pal Attia; Pedro F. Viana; Mona Nasseri; Mark P. Richardson; Benjamin H. Brinkmann

doi:10.1109/BIBM52615.2021.9669843

Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks: Algorithm Development and Optimization

Tal Pal Attia, Pedro F. Viana, Mona Nasseri, Mark P. Richardson, Benjamin H. Brinkmann

Neurology

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

Abstract

Seizure forecasting is a great research interest due to its potential in helping patients manage activities or facilitate targeted therapies, specifically with the emergence of new subcutaneous continuous EEG recording systems that have shown promise to be helpful. In work presented here, we used one subject diagnosed with refractory epilepsy with 230 days of monitoring to evaluate seven architectures to design a seizure prediction algorithm using a deep learning RNN classifier. The preliminary results suggest that it is possible to forecast seizures using two-channel chronic subcutaneous EEG recordings. With an average AUC of 0.74947 for architectures found to have better than chance performance. Future work will focus on extending results to additional patients, investigating cross-subject performance, and the importance of the different inputs to the architectures.

Original language	English (US)
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3599-3602
Number of pages	4
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669843
State	Published - 2021
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: Dec 9 2021 → Dec 12 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	12/9/21 → 12/12/21

Keywords

Epilepsy
LSTM Neural Networks
Seizure Forecasting
subcutaneous EEG

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Biomedical Engineering
Health Informatics
Information Systems and Management

Access to Document

10.1109/BIBM52615.2021.9669843

Cite this

Attia, T. P., Viana, P. F., Nasseri, M., Richardson, M. P., & Brinkmann, B. H. (2021). Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks: Algorithm Development and Optimization. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 3599-3602). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669843

Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks : Algorithm Development and Optimization. / Attia, Tal Pal; Viana, Pedro F.; Nasseri, Mona et al.

Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 3599-3602 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

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

Attia, TP, Viana, PF, Nasseri, M, Richardson, MP & Brinkmann, BH 2021, Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks: Algorithm Development and Optimization. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 3599-3602, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 12/9/21. https://doi.org/10.1109/BIBM52615.2021.9669843

Attia TP, Viana PF, Nasseri M, Richardson MP, Brinkmann BH. Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks: Algorithm Development and Optimization. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 3599-3602. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669843

Attia, Tal Pal ; Viana, Pedro F. ; Nasseri, Mona et al. / Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks : Algorithm Development and Optimization. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 3599-3602 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

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title = "Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks: Algorithm Development and Optimization",

abstract = "Seizure forecasting is a great research interest due to its potential in helping patients manage activities or facilitate targeted therapies, specifically with the emergence of new subcutaneous continuous EEG recording systems that have shown promise to be helpful. In work presented here, we used one subject diagnosed with refractory epilepsy with 230 days of monitoring to evaluate seven architectures to design a seizure prediction algorithm using a deep learning RNN classifier. The preliminary results suggest that it is possible to forecast seizures using two-channel chronic subcutaneous EEG recordings. With an average AUC of 0.74947 for architectures found to have better than chance performance. Future work will focus on extending results to additional patients, investigating cross-subject performance, and the importance of the different inputs to the architectures. ",

keywords = "Epilepsy, LSTM Neural Networks, Seizure Forecasting, subcutaneous EEG",

author = "Attia, {Tal Pal} and Viana, {Pedro F.} and Mona Nasseri and Richardson, {Mark P.} and Brinkmann, {Benjamin H.}",

note = "Funding Information: This work was funded by the {\textquoteleft}My Seizure Gauge{\textquoteright} grant provided by the Epilepsy Innovation Institute, a research program of the Epilepsy Foundation of America. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669843",

language = "English (US)",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3599--3602",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, {Xiaohua Tony} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

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TY - GEN

T1 - Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks

T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

AU - Attia, Tal Pal

AU - Viana, Pedro F.

AU - Nasseri, Mona

AU - Richardson, Mark P.

AU - Brinkmann, Benjamin H.

N1 - Funding Information: This work was funded by the ‘My Seizure Gauge’ grant provided by the Epilepsy Innovation Institute, a research program of the Epilepsy Foundation of America. Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - Seizure forecasting is a great research interest due to its potential in helping patients manage activities or facilitate targeted therapies, specifically with the emergence of new subcutaneous continuous EEG recording systems that have shown promise to be helpful. In work presented here, we used one subject diagnosed with refractory epilepsy with 230 days of monitoring to evaluate seven architectures to design a seizure prediction algorithm using a deep learning RNN classifier. The preliminary results suggest that it is possible to forecast seizures using two-channel chronic subcutaneous EEG recordings. With an average AUC of 0.74947 for architectures found to have better than chance performance. Future work will focus on extending results to additional patients, investigating cross-subject performance, and the importance of the different inputs to the architectures.

AB - Seizure forecasting is a great research interest due to its potential in helping patients manage activities or facilitate targeted therapies, specifically with the emergence of new subcutaneous continuous EEG recording systems that have shown promise to be helpful. In work presented here, we used one subject diagnosed with refractory epilepsy with 230 days of monitoring to evaluate seven architectures to design a seizure prediction algorithm using a deep learning RNN classifier. The preliminary results suggest that it is possible to forecast seizures using two-channel chronic subcutaneous EEG recordings. With an average AUC of 0.74947 for architectures found to have better than chance performance. Future work will focus on extending results to additional patients, investigating cross-subject performance, and the importance of the different inputs to the architectures.

KW - Epilepsy

KW - LSTM Neural Networks

KW - Seizure Forecasting

KW - subcutaneous EEG

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T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

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BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

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A2 - Luo, Feng

A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

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A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 9 December 2021 through 12 December 2021

ER -

Seizure Forecasting from Subcutaneous EEG Using Long Short Term Memory Neural Networks: Algorithm Development and Optimization

Abstract

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Keywords

ASJC Scopus subject areas

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