Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection

Abstract

Electroencephalography signals obtained from the brain‘s electrical activity are commonly used for the diagnosis of neurological diseases. These signals indicate the electrical activity in the brain and contain information about the brain. Epilepsy, one of the most important diseases in the brain, manifests itself as a result of abnormal pathological oscillating activity of a group of neurons in the brain. Automated systems that employed the electroencephalography signals are being developed for the assessment and diagnosis of epileptic seizures. The aim of this study is to focus on the effectiveness of stacking ensemble approach based model for predicting whether there is epileptic seizure or not. So, this study enables the readers and researchers to examine the proposed stacking ensemble model. The benchmark clinical dataset provided by Bonn University was used to assess the proposed model. Comparative experiments were conducted by utilizing the proposed model and the base deep neural networks model to show the effectiveness of the proposed model for seizure detection. Experiments show that the proposed model is proven to be competitive to base DNN model. The results indicate that the performance of the epileptic seizure detection by the stacking ensemble based deep neural networks model is high; especially the average accuracy value of 97.17%. Also, its average sensitivity with 93.11% is superior to the base DNN model. Thus, it can be said that the proposed model can be included in an expert system or decision support system. In this context, this system would be precious for the clinical diagnosis and treatment of epilepsy.