research-article

A Deep Learning Approach to Phase-Space Analysis for Seizure Detection

Authors:

Patrick Luckett,

J. Todd McDonald,

Ryan BentonAuthors Info & Claims

BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

Pages 190 - 196

https://doi.org/10.1145/3307339.3342131

Published: 04 September 2019 Publication History

Abstract

Many epileptic patients do not respond to medication or surgery. Recent technology has demonstrated that closed-loop responsive neurostimulation therapy is a realistic treatment for epileptic patients. However, ambulatory care of epileptic patients requires a highly accurate automated seizure detection algorithm. In this research, we implement a method for epileptic seizure detection based on nonlinear phase space analysis and deep convolutional neural networks (CNN). The underlying dynamics of scalp electroencephalography (sEEG) are extracted through time delay embedding and phase-space reconstruction. These features are used for training a CNN with a regression output to predict time until seizure. In experiments using EEG data collected in clinical environments from forty patients, our deep learning approach achieved high accuracy in predicting time until seizure onset, with a root mean squared error (RMSE) of 14.1 minutes and adjusted R-squared of .95 on out of sample testing data.

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Cited By

Zhang JZheng SChen WDu GFu QJiang H(2024)A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and predictionScientific Reports10.1038/s41598-024-67855-414:1Online publication date: 23-Jul-2024
https://doi.org/10.1038/s41598-024-67855-4
Pandey BKumar Pandey DPratap Mishra BRhmann W(2022)A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directionsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.01.00734:8(5083-5099)Online publication date: Sep-2022
https://doi.org/10.1016/j.jksuci.2021.01.007
Adlakha SChhabra DShukla P(2020)Effectiveness of gamification for the rehabilitation of neurodegenerative disordersChaos, Solitons & Fractals10.1016/j.chaos.2020.110192140(110192)Online publication date: Nov-2020
https://doi.org/10.1016/j.chaos.2020.110192

Index Terms

A Deep Learning Approach to Phase-Space Analysis for Seizure Detection
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics

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cover image ACM Conferences

BCB '19: Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 2019

716 pages

ISBN:9781450366663

DOI:10.1145/3307339

General Chairs:
Xinghua (Mindy) Shi
Temple University, USA
,
Michael Buck
University of Buffalo, USA
,
Program Chairs:
Jian Ma
Carnegie Mellon University, USA
,
Pierangelo Veltri
University Magna Graecia of Catanzaro, Italy

Copyright © 2019 ACM.

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Published: 04 September 2019

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BCB '19: 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

September 7 - 10, 2019

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Cited By

Zhang JZheng SChen WDu GFu QJiang H(2024)A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and predictionScientific Reports10.1038/s41598-024-67855-414:1Online publication date: 23-Jul-2024
https://doi.org/10.1038/s41598-024-67855-4
Pandey BKumar Pandey DPratap Mishra BRhmann W(2022)A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directionsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.01.00734:8(5083-5099)Online publication date: Sep-2022
https://doi.org/10.1016/j.jksuci.2021.01.007
Adlakha SChhabra DShukla P(2020)Effectiveness of gamification for the rehabilitation of neurodegenerative disordersChaos, Solitons & Fractals10.1016/j.chaos.2020.110192140(110192)Online publication date: Nov-2020
https://doi.org/10.1016/j.chaos.2020.110192

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