Detection of Epileptic Seizures using Unsupervised Learning Techniques for Feature Extraction

Automatic epileptic seizure prediction from EEG (electroencephalogram) data is a challenging problem. This is due to the complex nature of the signal itself and of the generated abnormalities. In this paper, we investigate several deep network architectures i.e. stacked autoencoders and convolutional networks, for unsupervised EEG feature extraction. The proposed EEG features are used to solve the prediction of epileptic seizures via Support Vector Machines. This approach has many benefits: (i) it allows to achieve a high accuracy using small size sample data, e.g. 1 second EEG data; (ii) features are determined in an unsupervised manner, without the need for manual selection. Experimental validation is carried out on real-world data, i.e. the CHB-MIT dataset. We achieve an overall accuracy, sensitivity and specificity of up to 92%, 95% and 90% respectively.

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