research-article

Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization

Authors:

M. Shamim Hossain,

Syed Umar Amin,

Mansour Alsulaiman,

Ghulam MuhammadAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 15, Issue 1s

Article No.: 10, Pages 1 - 17

https://doi.org/10.1145/3241056

Published: 17 February 2019 Publication History

Abstract

Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn robust features from raw Electroencephalogram (EEG) data to detect seizures. Seizures are hard to detect, as they vary both inter- and intra-patient. In this article, we use a deep CNN model for seizure detection task on an open-access EEG epilepsy dataset collected at the Boston Children's Hospital. Our deep learning model is able to extract spectral, temporal features from EEG epilepsy data and use them to learn the general structure of a seizure that is less sensitive to variations. For cross-patient EEG data, our method produced an overall sensitivity of 90.00%, specificity of 91.65%, and overall accuracy of 98.05% for the whole dataset of 23 patients. The system can detect seizures with an accuracy of 99.46%. Thus, it can be used as an excellent cross-patient seizure classifier. The results show that our model performs better than the previous state-of-the-art models for patient-specific and cross-patient seizure detection task. The method gave an overall accuracy of 99.65% for patient-specific data. The system can also visualize the special orientation of band power features. We use correlation maps to relate spectral amplitude features to the output in the form of images. By using the results from our deep learning model, this visualization method can be used as an effective multimedia tool for producing quick and relevant brain mapping images that can be used by medical experts for further investigation.

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https://doi.org/10.57197/JDR-2024-0042
Selvaraj SThangavel SPrabhakaran MSathish T(2024)Impressive predictive model for Breast Cancer based on Machine LearningEAI Endorsed Transactions on Pervasive Health and Technology10.4108/eetpht.10.524610Online publication date: 29-Feb-2024
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Zhou WZheng WFeng YLi X(2024)LMA-EEGNet: A Lightweight Multi-Attention Network for Neonatal Seizure Detection Using EEG signalsElectronics10.3390/electronics1312235413:12(2354)Online publication date: 16-Jun-2024
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Index Terms

Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
  2. Machine learning
2. Social and professional topics
  1. Computing / technology policy
    1. Medical information policy
      1. Medical technologies

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 15, Issue 1s

Special Section on Deep Learning for Intelligent Multimedia Analytics and Special Section on Multi-Modal Understanding of Social, Affective and Subjective Attributes of Data

January 2019

265 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3309769

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 17 February 2019

Accepted: 01 July 2018

Revised: 01 July 2018

Received: 01 October 2017

Published in TOMM Volume 15, Issue 1s

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https://doi.org/10.57197/JDR-2024-0042
Selvaraj SThangavel SPrabhakaran MSathish T(2024)Impressive predictive model for Breast Cancer based on Machine LearningEAI Endorsed Transactions on Pervasive Health and Technology10.4108/eetpht.10.524610Online publication date: 29-Feb-2024
https://doi.org/10.4108/eetpht.10.5246
Zhou WZheng WFeng YLi X(2024)LMA-EEGNet: A Lightweight Multi-Attention Network for Neonatal Seizure Detection Using EEG signalsElectronics10.3390/electronics1312235413:12(2354)Online publication date: 16-Jun-2024
https://doi.org/10.3390/electronics13122354
Sánchez-Hernández STorres-Ramos SRomán-Godínez ISalido-Ruiz R(2024)Evaluation of the Relation between Ictal EEG Features and XAI ExplanationsBrain Sciences10.3390/brainsci1404030614:4(306)Online publication date: 25-Mar-2024
https://doi.org/10.3390/brainsci14040306
Cheng LXiong JDuan JZhang YChen CZhong JZhou ZQuan Y(2024)SaE-GBLS: an effective self-adaptive evolutionary optimized graph-broad model for EEG-based automatic epileptic seizure detectionFrontiers in Computational Neuroscience10.3389/fncom.2024.137936818Online publication date: 11-Jul-2024
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Zhang GZhang ALiu HLuo JChen J(2024)Positional multi-length and mutual-attention network for epileptic seizure classificationFrontiers in Computational Neuroscience10.3389/fncom.2024.135878018Online publication date: 25-Jan-2024
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Ru YAn GWei ZChen H(2024)Epilepsy detection based on multi-head self-attention mechanismPLOS ONE10.1371/journal.pone.030516619:6(e0305166)Online publication date: 11-Jun-2024
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Yang YLuan TYu ZZhang MLi FChen XGao FZhang Z(2024)Technological Vanguard: the outstanding performance of the LTY-CNN model for the early prediction of epileptic seizuresJournal of Translational Medicine10.1186/s12967-024-04945-x22:1Online publication date: 16-Feb-2024
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Jibon FJamil Chowdhury AMiraz MJin HKhandaker MSultana SNur SSiddiqui FKamal ASalman MYoussef A(2024)Sequential graph convolutional network and DeepRNN based hybrid framework for epileptic seizure detection from EEG signalDIGITAL HEALTH10.1177/2055207624124987410Online publication date: 7-May-2024
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