Abstract
Seizure event detection by manually analyzing electroencephalogram (EEG) data is a routine process in epilepsy units done by trained professionals. Misdiagnosis of epileptic seizures is a common problem. Therefore, an automatic seizure detection algorithm can help neurologists in diagnosing epilepsy correctly in lesser time with higher accuracy. In this paper, we have proposed an automated seizure detection algorithm based on continuous wavelet transform (CWT) and convolutional neural networks (CNNs). Seizure and non-seizure events are classified from the University of Bonn Germany dataset. To train the Deep learning-based classifier efficiently, the amount of data is increased using a data augmentation technique. After that, the data is window segmented and continuous wavelet transform (CWT) is employed on these segments to get a scalogram plot. The image of the scalogram plot is used in training the CNN model for seizure classification. Effect of different window sizes (1 s, 2 s, and 3 s) on classification accuracy is analyzed, and 3 s window segments have shown best classification result on original and augmented data.
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Acknowledgements
I would like to thank Indian Institute of Technology Ropar for providing me the research fellowship and Department for Biomedical Engineering IIT Ropar for providing the facilities to carry out this research.
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Shukla, R., Kumar, B., Gaurav, G., Singh, G., Sahani, A.K. (2022). Epileptic Seizure Detection Using Continuous Wavelet Transform and Deep Neural Networks. In: Suryadevara, N.K., George, B., Jayasundera, K.P., Roy, J.K., Mukhopadhyay, S.C. (eds) Sensing Technology. Lecture Notes in Electrical Engineering, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-98886-9_23
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DOI: https://doi.org/10.1007/978-3-030-98886-9_23
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