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Research Area: Machine Learning
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.
Keywords:
Detection Of Epileptic Seizures
Unsupervised Learning Techniques
Feature Extraction
Machine Learning
Deep Learning
Author(s) Name: Alexandra-Maria Tăuţan; Mihai Dogariu; Bogdan Ionescu
Journal name:
Conferrence name: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Publisher name: IEEE
DOI: 10.1109/EMBC.2019.8856315
Volume Information:
