Epilepsy Detection from EEG Data Using a Hybrid CNN-LSTM Model

Abstract

‘An epileptic seizure’, a neurological disorder, occurs when electric burst travel over the brain, causing the person to lose control or consciousness. Anticipating epilepsy when the event happen is beneficial for epileptic control with medication or neurological pre-surgical planning. To detect epilepsy using electroencephalogram (EEG) data, machine learning and computational approaches are applied. Because of their better categorization skills, deep learning (DL) and machine learning (ML) approaches have recently been applied in the automated identification of epileptic events. ML and DL models can reliably diagnose diverse seizure disorders from vast EEG data and supply relevant findings for neurologists. To detect epilepsy, we developed a hybrid network that combines a ‘Convolutional Neural Network (CNN)’ and a ‘Long Term Short Term Memory (LSTM)’. Our dataset is divided into two categories: epilepsy and normal. CNN-LSTM has been used to train our algorithm. With the Adam optimizer, our proposed CNN-LSTM model achieves 94.98% training accuracy and 82.21% validation accuracy. We also evaluate our results to those of machine learning methods such as Decision Tree, Logistic Regression and Naive Bayes. The comparative results clearly reveal that our suggested CNN-LSTM classifier outperforms the other learners.