Automatic Identification of Epilepsy Seizures from EEG Signals Using a Hybrid CNN and LSTM Model

Pankaj Saraswat; Dr. Sandeep Chahal

doi:10.59367/t7q8kw09

Authors

Pankaj Saraswat Research Scholar, Department of Computer Science and Engineering, NIILM University, Kaithal, Haryana, India
Dr. Sandeep Chahal Professor, Department of Computer Science and Engineering, NIILM University, Kaithal, Haryana, India

DOI:

https://doi.org/10.59367/t7q8kw09

Abstract

The neurological illness known as epilepsy is characterized by a disruption in the
normal functioning of the brain and can be found in severe cases. It is estimated that more
than 10 percent of the whole population across the entire planet is affected by this ailment
every single day. When acquiring information on the brain's electrical activity,
electroencephalograms, often known as EEGs, are utilized rather frequently by researchers.
In this paper, an end-to-end system is proposed that utilises a combination of two deep
learning models, namely Convolutional Neural Networks (CNNs) and Long Short-Term
Memory Networks (LSTM), to classify electroencephalogram (EEG) data of epilepsydisordered
people into three distinct categories: preictal, normal, and seizure. The findings
of the experiment were obtained by making use of a dataset that is well-known and easily
available, which Bonn International University provided. Within this CNN-LSTM
classification model, the tasks of feature extraction, selection, and classification are all
carried out in an automated fashion. Because of this, there is no longer a requirement for a
manually devised methodology for feature extraction. In this study, the performance of the
CNN-LSTM model is studied and assessed with relation to specificity, sensitivity, and
accuracy. This is accomplished through the usage of the 10-fold cross-validation approach.
The accuracy is 99.33%, the sensitivity is 99.33%, and the specificity is 99.66%
concurrently, as indicated by the findings that were gathered while the trials were being
carried out and the data that were collected. The results of our research indicate that deep
learning approaches are the most appropriate choices for categorization when compared to
other methods that are currently deemed to be state-of-the-art.

References

. National Cancer Institute. (2021). SEER Cancer Stat Facts: Leukemia.

https://seer.cancer.gov/statfacts/html/leuks.html

. Paswan, S., & Rathore, Y. K. (2017). Detection and classification of blood cancer from microscopic cell images using SVM KNN and NN classifier. Int. J. Adv. Res. Ideas Innov. Technol, 3, 315-324.

. Preskill, J. (2018). Quantum Computing in the NISQ era and beyond. Quantum, 2, 79.

. Benedetti, M., et al. (2020). Quantum-inspired models in machine learning: from quantum

computing to quantum-inspired computing. arXiv:2004.12238.

. Chen, H., et al. (2023). Quantum-inspired algorithms for genomic data analysis: a review. Briefings in Bioinformatics, 24(3), 567–579.

. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

. Shafique, S., & Tehsin, S. (2018). Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technology in Cancer Research & Treatment, 17, 1533033818802789.

. Khadatkar, D. R., & Patra, J. P. (2023, December). Comparative Analysis of Different Machine Learning Algorithms for Detection of Alzheimer Disease from Medical images. In 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries

(ICAIIHI) (Vol. 1, pp. 1-5). IEEE.

. Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019). Classification of histopathological biopsy images using ensemble of deep learning networks. arXiv preprint arXiv:1909.11870.

. Kouzehkanan, S. Z. M., Saghari, S., Tavakoli, I., Rostami, P., Karami, M., Moradi, G., & Rastgou, A. (2021). A novel method for white blood cells detection and classification in peripheral blood smear images. Scientific Reports, 11(1), 1-18.

. Wang, Y., Wei, X. S., Cui, F., Shao, S., Zhang, T., Zhang, L., & Zhou, Y. (2020). A deep-transfer learning approach for novel and rare blood cell classification. IEEE Journal of Biomedical and Health Informatics, 25(8), 3035-3045.

. Gehlot, S., Gupta, A., & Gupta, R. (2020). SDCT-AuxNet θ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis. Medical Image Analysis, 61, 101661.

. Jiang, Y., Chen, L., Zhang, H., & Xiao, X. (2020). Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS One, 15(3), e0230287.

. Li, Y., Cheng, H., Zhou, Z., & Tian, J. (2021). Iteratively-refined interactive 3D medical image segmentation with multi-agent reinforcement learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9394-9402).

. Shahin, A. I., Guo, Y., Amin, K. M., & Sharawi, A. A. (2019). White blood cells identification system based on convolutional deep neural learning networks. Computer Methods and Programs in Biomedicine, 168, 69-80.