Volume 4, Issue 3, September 2019, Page: 39-44
Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data
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
Received: Oct. 14, 2019;       Accepted: Nov. 15, 2019;       Published: Nov. 21, 2019
DOI: 10.11648/j.mlr.20190403.11      View  538      Downloads  110
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.
Epileptic Seizure Detection, Machine Learning, Electroencephalography, Convolutional Neural Network, Recurrent Neural Network
To cite this article
Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li, Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data, Machine Learning Research. Vol. 4, No. 3, 2019, pp. 39-44. doi: 10.11648/j.mlr.20190403.11
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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