Epileptic Seizure Detection using Deep Learning Approach
DOI:
https://doi.org/10.21928/uhdjst.v3n2y2019.pp41-50Keywords:
Electroencephalogram, Epilepsy, Epileptic Seizure, Long- and short-term Memory, Seizure DetectionAbstract
An epileptic seizure is a sign of abnormal activity in the human brain. Electroencephalogram (EEG) is a standard tool that has been used vastly for detection of seizure activities. Many methods have been developed to help the neurophysiologists to detect the seizure activities with high accuracy. Most of them rely on the features extracted in the time, frequency, or time-frequency domains. The performance of the proposed methods is related to the performance of the features extracted from EEG recordings. Deep neural networks enable learning directly on the data without the domain knowledge needed to construct a feature set. This approach has been hugely successful in almost all machine learning applications. We propose a new framework that also learns directly from the data, without extracting a feature set. We proposed an original deep-learning-based method to classify EEG recordings. The EEG signal is segmented into 4 s segments and used to train the long- and short-term memory network. The trained model is used to discriminate the EEG seizure from the background. The Freiburg EEG dataset is used to assess the performance of the classifier. The 5-fold cross-validation is selected for evaluating the performance of the proposed method. About 97.75% of the accuracy is achieved.
References
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[2] S. M. Usman, M. Usman and S. Fong. “Epileptic seizures prediction using machine learning methods”. Computational and Mathematical Methods in Medicine, vol. 2017, pp. 1-10, 2017.
[3] R. Hussein, H. Palangi, R. Ward and Z. J. Wang. Epileptic seizure detection: A deep learning approach. Electrical Engineering and Systems Science, vol. 16, pp. 53, 2018.
[4] U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan and H. Adeli. “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals”. Computer Biology and Medicine, vol. 100, pp. 270-278, 2018.
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[6] Y. Paul. “Various epileptic seizure detection techniques using biomedical signals: A review”. Brain Informatics, vol. 5, no. 2,pp. 6, 2018.
[7] A. X. M. Chang, B. Martini and E. Culurciello. “Recurrent Neural Networks Hardware Implementation on FPGA”. IEEE International Symposium on Circuits and Systems, 2015.
[8] M. Mohammadi, A. A. Pouyan, N. A. Khan and V. Abolghasemi. “Locally optimized adaptive directional time frequency distributions”. Circuits, Systems and Signal Processing, vol. 37, no. 8, pp. 3154-3174, 2018.
[9] N. A. Khan, M. Mohammadi and S. Ali. “Instantaneous frequency estimation of intersecting and close multi-component signals with varying amplitudes”. Signal, Image and Video Processing, vol. 13, no. 3, pp. 517-524, 2019.
[10] N. A. Khan, M. Mohammadi and I. Djurović. “A modified viterbi algorithm-based if estimation algorithm for adaptive directional time frequency distributions”. Circuits, Systems and Signal Processing, vol. 38, no. 5, pp. 2227-2244, 2019.
[11] M. Mohammadi, A. A. Pouyan, V. Abolghasemi and N. A. Khan. “Radon transform for adaptive directional time-frequency distributions: Application to seizure detection in EEG signals”. Vol. 2017. Proceeding 3rd Iran. Conference Signal Processing Intelligence Systems ICSPIS 2017, 2018, pp. 5-10.
[12] M. Mohammadi, A. A. Pouyan, V. Abolghasemi and N. A. Khan. “Enhancement of the spikes attributes in the time-frequency representations of real EEG signals”. Vol. 2018. 2017 IEEE 4th International Conference Knowledge-Based Engineering and Innovation KBEI 2017, 2018, pp. 0768-0772.
[13] M. Mohammadi and H. Mahmud. “Review a rticl e the state of the art in feature extraction methods for electroencephalogram epileptic classification”. UHD Journal of Science and Technology, vol. 3, no. 2, pp. 16-23, 2019.
[14] M. Mohammadi, N. A. Khan and A. A. Pouyan. “Automatic seizure detection using a highly adaptive directional time frequency distribution”. Multidimensional Systems and Signal Processing, vol. 29, no. 4, pp. 1661-1678, 2018.
[15] P. Ghaderyan, A. Abbasi and M. H. Sedaaghi. “An efficient seizure prediction method using KNN-based undersampling and linear frequency measures”. Journal of Neuroscience Methods, vol. 232, pp. 134-142, 2014.
[16] Y. X. Zheng, J. M. Zhu, Y. Qi, X. X. Zheng and J. M. Zhang. “An automatic patient-specific seizure onset detection method using intracranial electroencephalography”. Neuromodulation, vol. 18, no. 2, pp. 79-84, 2015.
[17] A. G. Correa, L. Orosco, P. Diez and E. Laciar. “Automatic detection of epileptic seizures in long-term EEG records,” Computers in Biology and Medicine, vol. 57, pp. 66-73, 2015.
[18] S. Yuan, W. Zhou, Q. Wu and Y. Zhang. “Epileptic seizure detection with log-euclidean gaussian kernel-based sparse representation,” International Journal of Neural Systems, vol. 26, no. 3, p. 1650011, 2016.
[19] D. Geng, W. Zhou, Y. Zhang and S. Geng. “Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG”. Biocybernetics and Biomedical Engineering, vol. 36, no. 2, pp. 375-384, 2016.
[20] M. Z. Parvez and M. Paul. “Epileptic seizure prediction by xploiting spatiotemporal relationship of EEG signals using phase correlation”. IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 1, pp. 158-168, 2016.
[21] S. Jukic and A. Subasi. A mapreduce-based rotation forest classifier for epileptic seizure prediction. Clinical Orthopaedics and Related Research, vol. 1712, p. 49, 2017.
[22] B. Sharif and A. H. Jafari. “Prediction of epileptic seizures from EEG using analysis of ictal rules on poincaré plane”. Computer Methods and Programs in Biomedicine, vol. 145, pp. 11-22, 2017.
[23] M. Alfaro-Ponce, A. Argüelles, I. Chairez and A. Pérez. “Automatic electroencephalographic information classifier based on recurrent neural networks”. International Journal of Machine Learning and Cybernetics, vol. 2018, pp. 1-13, 2018.
[24] R. Hussein, H. Palangi, R. K. Ward and Z. J. Wang. “Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals”. Clinical Neurophysiology, vol. 130, no. 1, pp. 25-37, 2019.
[25] P. Li, C. Karmakar, J. Yearwood, S. Venkatesh, M. Palaniswami and C. Liu. “Detection of epileptic seizure based on entropy analysis of short-term EEG”. PLoS One, vol. 13, no. 3, pp. 1-17, 2018.
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[27] P. Barde and M. Barde. “What to use to express the variability of data: Standard deviation or standard error of mean”. Perspectives in Clinical Research, vol. 3, no. 3, p. 113, 2012.
[28] L. Frølich and I. Dowding. “Removal of muscular artifacts in EEG signals : A comparison of linear decomposition methods”. Brain Informatics, vol. 5, no. 1, pp. 13-22, 2018.
[29] Y. Lecun, Y. Bengio and G. Hinton. “Deep learning”. Nature, vol. 521, no. 7553, pp. 436-444, 2015.
[30] K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink and J. Schmidhuber. “LSTM: A search space odyssey”. IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2222-2232, 2017.
[31] E. Alickovic, J. Kevric and A. Subasi. “Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction”. Biomedical Signal Processing and Control, vol. 39, pp. 94-102, 2018.
[32] Z. Yu, W. Zhou, F. Zhang, F. Xu, S. Yuan, Y. Leng, Y. Li and Q. Yuan. “Automatic seizure detection based on kernel robust probabilistic collaborative representation”. Medical and Biological Engineering and Computing, vol. 50, no. 1, pp. 205-219, 2018.
[33] S. Xie and S. Krishnan. “Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis”. Medical and Biological Engineering and Computing, vol. 51, pp. 49-60, 2013.
[34] K. D. Tzimourta, A. T. Tzallas, N. Giannakeas and L.G. Astraksas. “A robust methodology for classification of epileptic seizures in EEG signals”. Health and Technology (Berl), vol. 9, no. 2, pp. 135-142, 2019.
[35] Z. Cui, S. Member, R. Ke, S. Member and Y. Wang. Deep stacked bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. Clinical Orthopaedics and Related Research, vol. 1801, pp. 1-12, 2018.
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Published
2019-08-21
How to Cite
Jaafar, S. T., & Mohammadi, M. (2019). Epileptic Seizure Detection using Deep Learning Approach. UHD Journal of Science and Technology, 3(2), 41–50. https://doi.org/10.21928/uhdjst.v3n2y2019.pp41-50
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