A Novel CNN-FRTAM Model for Enhanced Detection of Epileptic Seizures in EEG Signals Utilizing Deep Learning and Continuous Wavelet Transform

This study presents a novel approach to enhancing electroencephalography (EEG) signal classification for improved epileptic seizure detection. Traditional techniques for seizure detection often rely on manual analysis and suffer from high computational burdens and bias. Integrating deep learning with the continuous wavelet transform (CWT) is proposed to address these limitations for effective feature extraction. The CNN-FRTAM model, which integrates a convolutional neural network (CNN) and a frequency-region temporal attention mechanism (FRTAM), employs rigorous pre-processing of EEG signals to optimize performance. Extensive evaluation on a diverse dataset revealed an accuracy of 99.80% in multi-class classification and 99.90% in binary classification between Normal and Abnormal states. The CNN-FRTAM model significantly outperformed traditional architectures such as InceptionV3, VGG19, and ResNet50, demonstrating its potential for effective real-time applications in clinical epileptic seizure management. By opening up new avenues for accurate seizure detection, this work contributes to improving patient outcomes in epilepsy care.