Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data

Haotian Liu; Lin Xi; Ying Zhao; Zhixiang Li

doi:doi:10.11648/j.mlr.20190403.11

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Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li

Published in Machine Learning Research (Volume 4, Issue 3)

Received: 14 October 2019 Accepted: 15 November 2019 Published: 21 November 2019

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Abstract

Epileptic seizure is associated with significant morbidity diseases and mortality. An early identification of seizure activity can help prevent patients from adverse outcomes. Electroencephalography (EEG) raw data is a good source to recognize epileptic seizure from other brain activities. Numerous previous studied have applied feature engineering techniques to extract clinical meaningful features in order to indentify Seizure from EEG raw data. However, these techniques required intensive clinical, radiology and engineering expertise. In this study, we applied 6 machine learning algorithms (including naïve bayes, logistic regression, support vector machine, random forest and K-nearest neighbours and gradient boosting decision trees) and 3 deep learning architecture (including convolutional neural network (CNN), long-short term network (LSTM) and Gated Recurrent Unit (GRU)) to conduct binary and multi-label brain activities classification. Our best results of binary classification yielded that ensemble classifiers can classify seizure from other activities with a high accuracy and AUC over 0.96. In multi-label classification, both GRU and RNN yielded an averaged accuracy over 0.7. A compared study was also presented to analyze the performance of each configuration. In conclusion, machine learning and deep learning demonstrated their potential usage in epileptic seizure identification using EEG raw data. Future work may be experimented in a larger dataset to enable the seizure identification at a timely manner.

Published in	Machine Learning Research (Volume 4, Issue 3)
DOI	10.11648/j.mlr.20190403.11
Page(s)	39-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Epileptic Seizure Detection, Machine Learning, Electroencephalography, Convolutional Neural Network, Recurrent Neural Network

References

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APA Style

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li. (2019). Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data. Machine Learning Research, 4(3), 39-44. https://doi.org/10.11648/j.mlr.20190403.11

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ACS Style

Haotian Liu; Lin Xi; Ying Zhao; Zhixiang Li. Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data. Mach. Learn. Res. 2019, 4(3), 39-44. doi: 10.11648/j.mlr.20190403.11

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AMA Style

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li. Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data. Mach Learn Res. 2019;4(3):39-44. doi: 10.11648/j.mlr.20190403.11

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@article{10.11648/j.mlr.20190403.11,
  author = {Haotian Liu and Lin Xi and Ying Zhao and Zhixiang Li},
  title = {Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data},
  journal = {Machine Learning Research},
  volume = {4},
  number = {3},
  pages = {39-44},
  doi = {10.11648/j.mlr.20190403.11},
  url = {https://doi.org/10.11648/j.mlr.20190403.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20190403.11},
  abstract = {Epileptic seizure is associated with significant morbidity diseases and mortality. An early identification of seizure activity can help prevent patients from adverse outcomes. Electroencephalography (EEG) raw data is a good source to recognize epileptic seizure from other brain activities. Numerous previous studied have applied feature engineering techniques to extract clinical meaningful features in order to indentify Seizure from EEG raw data. However, these techniques required intensive clinical, radiology and engineering expertise. In this study, we applied 6 machine learning algorithms (including naïve bayes, logistic regression, support vector machine, random forest and K-nearest neighbours and gradient boosting decision trees) and 3 deep learning architecture (including convolutional neural network (CNN), long-short term network (LSTM) and Gated Recurrent Unit (GRU)) to conduct binary and multi-label brain activities classification. Our best results of binary classification yielded that ensemble classifiers can classify seizure from other activities with a high accuracy and AUC over 0.96. In multi-label classification, both GRU and RNN yielded an averaged accuracy over 0.7. A compared study was also presented to analyze the performance of each configuration. In conclusion, machine learning and deep learning demonstrated their potential usage in epileptic seizure identification using EEG raw data. Future work may be experimented in a larger dataset to enable the seizure identification at a timely manner.},
 year = {2019}
}

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TY - JOUR
T1 - Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data
AU - Haotian Liu
AU - Lin Xi
AU - Ying Zhao
AU - Zhixiang Li
Y1 - 2019/11/21
PY - 2019
N1 - https://doi.org/10.11648/j.mlr.20190403.11
DO - 10.11648/j.mlr.20190403.11
T2 - Machine Learning Research
JF - Machine Learning Research
JO - Machine Learning Research
SP - 39
EP - 44
PB - Science Publishing Group
SN - 2637-5680
UR - https://doi.org/10.11648/j.mlr.20190403.11
AB - Epileptic seizure is associated with significant morbidity diseases and mortality. An early identification of seizure activity can help prevent patients from adverse outcomes. Electroencephalography (EEG) raw data is a good source to recognize epileptic seizure from other brain activities. Numerous previous studied have applied feature engineering techniques to extract clinical meaningful features in order to indentify Seizure from EEG raw data. However, these techniques required intensive clinical, radiology and engineering expertise. In this study, we applied 6 machine learning algorithms (including naïve bayes, logistic regression, support vector machine, random forest and K-nearest neighbours and gradient boosting decision trees) and 3 deep learning architecture (including convolutional neural network (CNN), long-short term network (LSTM) and Gated Recurrent Unit (GRU)) to conduct binary and multi-label brain activities classification. Our best results of binary classification yielded that ensemble classifiers can classify seizure from other activities with a high accuracy and AUC over 0.96. In multi-label classification, both GRU and RNN yielded an averaged accuracy over 0.7. A compared study was also presented to analyze the performance of each configuration. In conclusion, machine learning and deep learning demonstrated their potential usage in epileptic seizure identification using EEG raw data. Future work may be experimented in a larger dataset to enable the seizure identification at a timely manner.
VL - 4
IS - 3
ER -

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Author Information

Haotian Liu

Northeast Yucai Foreign Language School, Shenyang, China
Lin Xi

Northeast Yucai Foreign Language School, Shenyang, China
Ying Zhao

Department of Engineering Science and Applied Math, Northwestern University, Evanston, the United States
Zhixiang Li

Department of Biomedical Engineering, Shenyang Pharmaceutical University, Shenyang, China

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Plain Text BibTeX RIS

APA Style

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li. (2019). Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data. Machine Learning Research, 4(3), 39-44. https://doi.org/10.11648/j.mlr.20190403.11

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ACS Style

Haotian Liu; Lin Xi; Ying Zhao; Zhixiang Li. Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data. Mach. Learn. Res. 2019, 4(3), 39-44. doi: 10.11648/j.mlr.20190403.11

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AMA Style

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li. Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data. Mach Learn Res. 2019;4(3):39-44. doi: 10.11648/j.mlr.20190403.11

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@article{10.11648/j.mlr.20190403.11,
  author = {Haotian Liu and Lin Xi and Ying Zhao and Zhixiang Li},
  title = {Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data},
  journal = {Machine Learning Research},
  volume = {4},
  number = {3},
  pages = {39-44},
  doi = {10.11648/j.mlr.20190403.11},
  url = {https://doi.org/10.11648/j.mlr.20190403.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20190403.11},
  abstract = {Epileptic seizure is associated with significant morbidity diseases and mortality. An early identification of seizure activity can help prevent patients from adverse outcomes. Electroencephalography (EEG) raw data is a good source to recognize epileptic seizure from other brain activities. Numerous previous studied have applied feature engineering techniques to extract clinical meaningful features in order to indentify Seizure from EEG raw data. However, these techniques required intensive clinical, radiology and engineering expertise. In this study, we applied 6 machine learning algorithms (including naïve bayes, logistic regression, support vector machine, random forest and K-nearest neighbours and gradient boosting decision trees) and 3 deep learning architecture (including convolutional neural network (CNN), long-short term network (LSTM) and Gated Recurrent Unit (GRU)) to conduct binary and multi-label brain activities classification. Our best results of binary classification yielded that ensemble classifiers can classify seizure from other activities with a high accuracy and AUC over 0.96. In multi-label classification, both GRU and RNN yielded an averaged accuracy over 0.7. A compared study was also presented to analyze the performance of each configuration. In conclusion, machine learning and deep learning demonstrated their potential usage in epileptic seizure identification using EEG raw data. Future work may be experimented in a larger dataset to enable the seizure identification at a timely manner.},
 year = {2019}
}

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