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Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures

Published: 22 June 2020 Publication History

Abstract

Epilepsy is a chronic medical condition that involves abnormal brain activity causing patients to lose control of awareness or motor activity. As a result, detection of pre-ictal states, before the onset of a seizure, can be lifesaving. The problem is challenging because it is difficult to discern between electroencephalogram signals in pre-ictal states versus signals in normal inter-ictal states. There are three key challenges that have not been addressed previously: (1) the inconsistent performance of prediction models across patients, (2) the lack of perfect prediction to protect patients from any episode, and (3) the limited amount of pre-ictal labeled data for advancing machine learning methods. This article addresses these limitations through a novel approach that uses adversarial examples with optimized tuning of a combined convolutional neural network and gated recurrent unit. Compared to the state of the art, the results showed an improvement of 3x in model robustness as measured in reduced variations and superior accuracy of the area under the curve, with an average increase of 6.7%. The proposed method also exhibited superior performance with other advances in the field of machine learning and customized for epilepsy prediction including data augmentation with Gaussian noise and multitask learning.

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Published In

cover image ACM Transactions on Computing for Healthcare
ACM Transactions on Computing for Healthcare  Volume 1, Issue 3
July 2020
152 pages
EISSN:2637-8051
DOI:10.1145/3403604
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 22 June 2020
Online AM: 07 May 2020
Accepted: 01 March 2020
Revised: 01 March 2020
Received: 01 August 2019
Published in HEALTH Volume 1, Issue 3

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Author Tags

  1. Deep learning
  2. adversarial examples
  3. electroencephalogram
  4. epileptic seizure prediction
  5. multitask learning

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  • (2024)MTL-SSU: A Multi-Task Self-Supervised Learning Framework for Epileptic Seizure Prediction2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10821748(3565-3570)Online publication date: 3-Dec-2024
  • (2024)A spatiotemporal graph transformer approach for Alzheimer’s disease diagnosis with rs-fMRIComputers in Biology and Medicine10.1016/j.compbiomed.2024.108762178(108762)Online publication date: Aug-2024
  • (2024)Supervised and Unsupervised Deep Learning Approaches for EEG Seizure PredictionJournal of Healthcare Informatics Research10.1007/s41666-024-00160-x8:2(286-312)Online publication date: 16-Feb-2024
  • (2024)A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium‐ion batteriesEnergy Science & Engineering10.1002/ese3.182312:8(3390-3400)Online publication date: 19-Jul-2024
  • (2023)EEG Signal Analysis Approaches for Epileptic Seizure Event Prediction Using Deep Learning2023 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)10.23919/SoftCOM58365.2023.10271651(1-7)Online publication date: 21-Sep-2023
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  • (2023)Understanding Contexts and Challenges of Information Management for Epilepsy CareProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580949(1-15)Online publication date: 19-Apr-2023
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