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  19      Downloads  6
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.
Khushaba, R. N., et al., Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications, 2013. 40 (9): p. 3803-3812.
Teplan, M., Fundamentals of EEG measurement. Measurement science review, 2002. 2 (2): p. 1-11.
Paller, K. A., M. Kutas, and H. K. McIsaac, Monitoring conscious recollection via the electrical activity of the brain. Psychological Science, 1995. 6 (2): p. 107-111.
Burch, N. R., Automatic analysis of the electroencephalogram: a review and classification of systems. Electroencephalography and clinical neurophysiology, 1959. 11 (4): p. 827-834.
Allison, T., et al., Potentials evoked in human and monkey cerebral cortex by stimulation of the median nerve: a review of scalp and intracranial recordings. Brain, 1991. 114 (6): p. 2465-2503.
Annegers, J. F., W. A. Hauser, and S. B. Shirts, Heart disease mortality and morbidity in patients with epilepsy. Epilepsia, 1984. 25 (6): p. 699-704.
Qu, H. and J. Gotman, A patient-specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use as a warning device. IEEE transactions on biomedical engineering, 1997. 44 (2): p. 115-122.
Van de Vel, A., et al., Non-EEG seizure-detection systems and potential SUDEP prevention: state of the art. Seizure, 2013. 22 (5): p. 345-355.
Mirowski, P., et al., Classification of patterns of EEG synchronization for seizure prediction. Clinical neurophysiology, 2009. 120 (11): p. 1927-1940.
Subasi, A. and M. I. Gursoy, EEG signal classification using PCA, ICA, LDA and support vector machines. Expert systems with applications, 2010. 37 (12): p. 8659-8666.
Ocak, H., Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Applications, 2009. 36 (2): p. 2027-2036.
Liang, N.-Y., et al., Classification of mental tasks from EEG signals using extreme learning machine. International journal of neural systems, 2006. 16 (01): p. 29-38.
Sharma, M., et al., An automatic detection of focal EEG signals using new class of time–frequency localized orthogonal wavelet filter banks. Knowledge-Based Systems, 2017. 118: p. 217-227.
Vorobyov, S. and A. Cichocki, Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biological Cybernetics, 2002. 86 (4): p. 293-303.
Petrosian, A., et al., Recurrent neural network based prediction of epileptic seizures in intra-and extracranial EEG. Neurocomputing, 2000. 30 (1-4): p. 201-218.
Liu, L., W. Chen, and G. Cao. Prediction of neonatal amplitude-integrated EEG based on LSTM method. in 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2016. IEEE.
Petrosian, A. A., et al. Classification of epileptic EEG using neural network and wavelet transform. in Wavelet Applications in Signal and Image Processing IV. 1996. International Society for Optics and Photonics.
Asuncion, A. and D. Newman, UCI machine learning repository. 2007.
Abiodun, O. I., et al., State-of-the-art in artificial neural network applications: A survey. Heliyon, 2018. 4 (11): p. e00938.
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