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September 10, 2025Open Access

An Optimized Hybrid CNN-LSTM Model for Epileptic Seizure Detection and Prediction

Key Points

The hybrid cnn-lstm model achieves a remarkable accuracy of 98.5%, indicating its effectiveness in seizure detection and prediction.
Performance comparison shows this hybrid model surpasses standalone cnn (95.8%) and lstm (94.2%) models, revealing its superior capabilities.
Training was conducted on the publicly available chb-mit eeg dataset, utilizing hyperparameter optimization for enhanced results.
The low false positive rate of 1.06% underscores the model's reliability compared to individual cnn and lstm models, highlighting its clinical potential.

Abstract

Timely detection and prediction of epileptic seizures are critical for enabling rapid clinical intervention. Conventional Εlectroencephalogram (EEG) analysis, however, is labor-intensive and prone to inaccuracies, highlighting the need for automated solutions. This study proposes an optimized hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model that enhances seizure detection by integrating spatial feature extraction (CNN) with temporal pattern recognition (LSTM). The model was trained and validated using the publicly available CHB-MIT EEG dataset, with performance further improved through hyperparameter optimization and feature selection. Experimental results show that the hybrid model achieves an accuracy of 98.5%, outperforming standalone CNN (95.8%) and LSTM (94.2%) models. Moreover, the proposed hybrid model achieves a False Positive Rate (FPR) of only 1.06%, surpassing the individual CNN (5.32%) and LSTM (4.26%) models. These findings demonstrate the potential of the proposed hybrid model in real-time monitoring epileptic episodes application.

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