Shallow Sparse Autoencoder Based Epileptic Seizure Prediction.


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Epileptic patients ’ quality of life can be significantly improved by epileptic seizure prediction based on scalp electroencephalogram (EEG). With the advancement of brain e-health technologies, there is an essential need for a method that accurately predicts seizures while running on computing platforms with very low computing resources. Moreover, existing methods do not provide EEG analysis on an individual channel basis to identify the abnormalities in the data. In order to address this issue, we propose an efficient framework for patient-specific seizure prediction. A hybrid model comprising of a shallow autoencoder (AE) with only one hidden layer and a support vector machine (SVM) classifier has been developed. Both multi-channel and single channel EEG signal processing schemes have been developed. Generating a lower dimensional sparse signal with AE in the first stage and classifying the signal using SVM in the second stage are the two stages that the model separates into when processing EEG data. We initially train the AE to provide an optimum sparse signal and then use this sparse signal as input for an SVM classifier to categorize the EEG data. Using the 10-fold cross validation strategy, the proposed model tests 13 patients from the CHB-MIT dataset and achieves an average sensitivity of 98% and an average area under the curve (AUC) of 99%. We have compared our hybrid approach ’s performance with both deep learning models and traditional techniques. The proposed methodology outperforms state of the art seizure prediction methods, demonstrating its effectiveness.
Autoencoder,EEG,Epilepsy,Seizure prediction,Shallow Network
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