Efficient Epileptic Seizure Recognition System using the Multi-model Ensemble Method from EEG

As reported by the WHO, nearly 50 million people of all ages are affected by epilepsy globally. In the brain, epilepsy manifests as a condition that is marked by repeated, unpremeditated fits and other related problems, and epileptic seizures are hard to predict. The electroencephalogram (EEG) has evolved into an effective diagnostic tool for epilepsy and subsequent epileptic seizures. Visually analyzing an EEG chart helps determine seizures' duration and location. But it's time-consuming. To remedy this issue, we have developed a deep learning model-based framework. In this study, an EEG-based dataset is utilized to test and verify the binary classification tasks to predict and classify whether a patient is having an epileptic seizure. It is proposed to implement a deep learning-based multimodel ensemble model to be able to classify and identify EEG recordings showing or not epileptic seizures from EEG signals. The deep learning models considered are CNN, LSTM, and BiLSTM models, and the proposed ensemble model is composed of these three trained models with their best weights during training. The performance of these models is evaluated and compared by utilizing different metric analyses such as precision, recall, specificity, accuracy, and F1-score. The proposed model that we've made is the most accurate with 99.87% among all the models that have been experimented with in this research, and researching the different ways to solve the classification problem of epileptic seizures from EEG signals shows the best way to do it.