Neural Network Based Epileptic EEG Detection and Classification
Abstract Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatment are available for epilepsy. These treatments involve use of medicines. But these are not effective in controlling frequency of seizure. There is need of removal of affected region using surgery. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is used before surgery for locating affected region. This manual process using EEG graphs is time consuming and requires deep expertise. In the present paper, a model has been proposed that preserves the true nature of EEG signal in form of textual one dimensional vector. The proposed model achieves a state of art performance for Bonn University dataset giving an average sensitivity, specificity of 81% and 81.4% respectively for classification among all five classes. Also for binary classification achieving 99.9%, 99.5% score value for specificity and sensitivity instead of 2D models used by other researchers. Thus developed system will significantly help neurosurgeons in increasing their performance.
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[3] Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine, 100, 270-278.
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[8] Lian, J., Zhang, Y., Luo, R., Han, G., Jia, W., & Li, C. (2020). Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network. IEEE Access, 8, 40008-40017.
[9] Mao, W. L., Fathurrahman, H. I. K., Lee, Y., & Chang, T. W. (2020, January). EEG dataset classification using CNN method. In Journal of Physics: Conference Series (Vol. 1456, No. 1, p. 012017). IOP Publishing.
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[11] Yeola, L. A., & Satone, M. P. (2019). Deep Neural Network for the Automated Detection and Diagnosis of Seizure using EEG Signals.
[12] http://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition downloaded, Jan 2020.
[2] Abedin, M. Z., Akther, S., & Hossain, M. S. (2019, September). An Artificial Neural Network Model for Epilepsy Seizure Detection. In 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) (pp. 860-865). IEEE.
[3] Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine, 100, 270-278.
[4] Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 061907.
[5] Bhagat, P. N., Ramesh, K. S., & Patil, S. T. (2019). An automatic diagnosis of epileptic seizure based on optimization using Electroencephalography Signals. Journal of Critical Reviews, 6(5), 200-212.
[6] Choi, G., Park, C., Kim, J., Cho, K., Kim, T. J., Bae, H., ... & Chong, J. (2019, January). A novel multi-scale 3D CNN with deep neural network for epileptic seizure detection. In 2019 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-2). IEEE.
[7] Huang, C., Chen, W., & Cao, G. (2019, November). Automatic Epileptic Seizure Detection via Attention-Based CNN-BiRNN. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 660-663). IEEE.
[8] Lian, J., Zhang, Y., Luo, R., Han, G., Jia, W., & Li, C. (2020). Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network. IEEE Access, 8, 40008-40017.
[9] Mao, W. L., Fathurrahman, H. I. K., Lee, Y., & Chang, T. W. (2020, January). EEG dataset classification using CNN method. In Journal of Physics: Conference Series (Vol. 1456, No. 1, p. 012017). IOP Publishing.
[10] Wei, Z., Zou, J., Zhang, J., & Xu, J. (2019). Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain. Biomedical Signal Processing and Control, 53, 101551.
[11] Yeola, L. A., & Satone, M. P. (2019). Deep Neural Network for the Automated Detection and Diagnosis of Seizure using EEG Signals.
[12] http://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition downloaded, Jan 2020.
Gupta, S., Meena, J., & Gupta, O. (2020). Neural Network Based Epileptic EEG Detection and Classification. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(2), 23–32. https://doi.org/10.14201/ADCAIJ2020922332
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