Abstract
In this paper, the authors are classifying time series electroencephalogram (EEG) data as epilepsy or not. Epilepsy is a type neurological disorder which affects around one percentage of the people around the world. Epilepsy is diagnosed by electroencephalographic (EEG) reading of the brain. EEG monitors the electrical activity of the brain. Studying EEG is a time-consuming and tedious task and requires lots of patience and domain knowledge. Deep learning techniques can help us reduce and automate this task and make its detection easier for doctors which in turn can help doctors to give better treatment to the patient. In this paper, the authors propose a sequential one-dimensional convolutional neural network (CNN) and state-of-the-art architectures like one-dimensional inception module and one-dimensional ResNet module which will classify time series EEG data as epilepsy or not. Using these approaches, sequential model was able to outperform the state-of-the-art models and achieved a ROC-AUC of 0.98.
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Manocha, G., Rustagi, H., Singh, S.P., Jain, R., Nagrath, P. (2022). Epilepsy Seizure Classification Using One-Dimensional Convolutional Neural Networks. In: Sharma, N., Chakrabarti, A., Balas, V.E., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies, vol 71. Springer, Singapore. https://doi.org/10.1007/978-981-16-2937-2_12
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