Automatic Epilepsy Seizure Classification Using EEG Signals Based on the CNN-LSTM Model

Abstract

Epilepsy is a neurological disorder characterized by frequent seizures and abnormal brain activity. It is typically diagnosed by examining electroencephalogram (EEG) recordings from epilepsy patients. Early detection and careful monitoring of children with epilepsy are crucial to preventing damaging spikes before the onset of the first seizure. Traditionally, this condition is examined manually by medical experts, a time-consuming process, especially during prolonged recordings. Therefore, an automated method for diagnosing focal (abnormal) EEG signals is essential. This study proposes an efficient model to classify and provide insights into focal and nonfocal stages. The model is based on an Inception ResNet v2 architecture pooled with a Deep Adagrad (Adaptive Gradient Descent Algorithm) Long Short-Term Memory (LSTM) network. EEG signal features are extracted using the Inception and ResNet layers, and significant features are then trained with a deep convolutional neural network (CNN) integrated with an Adagrad-optimized LSTM layer to classify focal and nonfocal EEG signals. The results demonstrate that the model achieves an impressive 99.76% accuracy in automatically detecting epilepsy abnormalities.