Seizure Prediction using Generative Adversarial Networks for EEG Data Synthesis
|International Journal of Computer Science and Engineering|
|© 2022 by SSRG - IJCSE Journal|
|Volume 9 Issue 10|
|Year of Publication : 2022|
|Authors : Garv Agarwal, Sai Sanjeet, Bibhu Datta Sahoo|
How to Cite?
Garv Agarwal, Sai Sanjeet, Bibhu Datta Sahoo, "Seizure Prediction using Generative Adversarial Networks for EEG Data Synthesis," SSRG International Journal of Computer Science and Engineering , vol. 9, no. 10, pp. 1-7, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSE-V9I10P101
Epilepsy is a common neurological disease characterized by seizures. Automatic prediction of these seizures can help clinicians prepare for and manage patient seizures due to prior knowledge of seizure onset. Automatic seizure prediction is done using electroencephalography (EEG) data containing brain activity representing seizures. Deep learning classifiers have been attempted in predicting seizure onset but are hindered due to a lack of high-quality preictal data in the dataset compared to the amount of interictal data. Solutions to the issues of data scarcity and data imbalance have been tried, such as under-sampling and various oversampling methods; however, these methods have not been successful in creating ample data. We propose a DCGAN that generates synthetic high-quality preictal data for the seizure prediction task. The synthetic data is compared to random oversampling of preictal data on the CHB-MIT Scalp EEG database using a CNN classifier with a 5-12% improvement.
Electroencephalography, Preictal, Interictal, Generative Adversarial Networks, Seizure prediction.
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