EPILEPTIC SEIZURE DETECTION FROM EEG SIGNALS WITH RECURRENT NEURAL NETWORKS BASED CLASSIFICATION MODEL

Abstract

Epileptic seizures are a neurological disorder that occurs as a result of sudden and uncontrolled electrical activities of the non-contagious brain. This condition may cause the person to lose normal activities temporarily. Epileptic seizures are a severe disease that affects approximately 60 million people in the world, usually manifested by symptoms such as loss of consciousness, muscle twitching, sudden sensory changes, or behavioural changes [1]. Genetics, brain injury, hormonal fluctuations, infections, or metabolic problems are some of the possible causes of epileptic seizures. Although the severity and duration of the seizure varies from person to person, it is usually very short and rarely reaches a point where it endangers human life. However, such seizures need to be recognized as soon as possible in order to improve the quality of life of individuals and reduce the frequency of seizures. Epileptic seizures are a manageable disease with early diagnosis and appropriate treatment. Recognizing epileptic seizures begins with understanding a person's symptoms and triggering factors. These symptoms may include loss of consciousness, muscle twitches, sudden sensory changes, and behavioural changes. The symptoms of seizures, past medical history, and neurological examinations are essential in the diagnosis process. From past to present, many methods have been developed for the early diagnosis and detection of epileptic seizures [2]. One of these is analyzing the brain's neural activities using electroencephalography (EEG), which helps experts make a diagnosis. Although EEG signals are used as a powerful tool in epileptic seizure recognition, distinguishing the signals within them is both costly and requires highly expert experience. Therefore, this study proposed an automatic classification model for pre-processed EEG signals using Dual-Tree Complex Wavelet Transform (DT-CWT) based on deep learning-based Recurrent Neural Networks (RNN) architecture to assist experts. Compared to classical machine learning methods, deep learning-based models require less manual feature engineering because they perform data automatically thanks to deep networks instead of manually selecting and transforming the data features. These advantages make the model more general and flexible. The proposed model aims to classify EEG signals and detect epileptic seizures effectively and quickly in the early stages.

Keywords

• Deep Learning
• Epilepstic Seizure
• RNN
• BiLSTM
• CNN

References

1. B.A. Other. "Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals." Applied Sciences 7.4 (2017): 385.
2. S. A. Other, "Epileptic seizures detection using deep learning techniques: A review." International journal of environmental research and public health 18.11 (2021): 5780.
3. Rogers, G. (2010). R. Appleton and A. Marson 2009: Epilepsy: the facts, Oxford, UK: Oxford University Press. 186 pp,£ 9.99 paperback. ISBN: 9780199233687. Primary Health Care Research & Development, 11(4), 413-413.
4. Rudler, M., Marois, C., Weiss, N., Thabut, D., Navarro, V., & Brain-Liver Pitié-Salpêtrière Study Group. (2017). Status epilepticus in patients with cirrhosis: How to avoid misdiagnosis in patients with hepatic encephalopathy. Seizure, 45, 192-197.
5. Bao, F. S., Lie, D. Y. C., & Zhang, Y. (2008, November). A new approach to automated epileptic diagnosis using EEG and probabilistic neural network. In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (Vol. 2, pp. 482-486). IEEE.
6. Ahmad Mir, W., Izharuddin, & Nissar, I. (2020). Contribution of application of deep learning approaches on biomedical data in the diagnosis of neurological disorders: A review on recent findings. In Advances in Computational Intelligence, Security and Internet of Things: Second International Conference, ICCISIoT 2019, Agartala, India, December 13–14, 2019, Proceedings 2 (pp. 87-97). Springer Singapore.
7. Hussain, L. (2018). Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach. Cognitive neurodynamics, 12(3), 271-294.
8. Ravi Kumar, M., & Srinivasa Rao, Y. (2019). Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition. Cluster Computing, 22, 13521-13531.

9. Kocadagli, O., & Langari, R. (2017). Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations. Expert Systems with Applications, 88, 419-434.
10. Usman, S. M., Khalid, S., & Bashir, S. (2021). A deep learning based ensemble learning method for epileptic seizure prediction. Computers in Biology and Medicine, 136, 104710.
11. Rashed-Al-Mahfuz, M., Moni, M. A., Uddin, S., Alyami, S. A., Summers, M. A., & Eapen, V. (2021). A deep convolutional neural network method to detect seizures and characteristic frequencies using epileptic electroencephalogram (EEG) data. IEEE journal of translational engineering in health and medicine, 9, 1-12.
12. https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition?resource=download
13. Ahmad Mir, W., Izharuddin, & Nissar, I. (2020). Contribution of application of deep learning approaches on biomedical data in the diagnosis of neurological disorders: A review on recent findings. In Advances in Computational Intelligence, Security and Internet of Things: Second International Conference, ICCISIoT 2019, Agartala, India, December 13–14, 2019, Proceedings 2 (pp. 87-97). Springer Singapore.
14. Aslan, S. (2022). A novel TCNN–Bi‐LSTM deep learning model for predicting sentiments of tweets about COVID‐19 vaccines. Concurrency and Computation: Practice and Experience, 34(28), e7387.
15. Joshi, R., & Tekchandani, R. (2016, August). Comparative analysis of Twitter data using supervised classifiers. In 2016 International conference on inventive computation technologies (ICICT) (Vol. 3, pp. 1-6). IEEE.
16. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
17. Graves, A., Fernández, S., & Schmidhuber, J. (2005, September). Bidirectional LSTM networks for improved phoneme classification and recognition. In International conference on artificial neural networks (pp. 799-804). Berlin, Heidelberg: Springer Berlin Heidelberg