Enhancing Epilepsy Diagnosis with Deep Learning and Multi-channel Processing of EEG Signals

Epilepsy is a neurological disorder that seriously affects patients’ lives and health. The accurate identification of epilepsy species is essential for developing effective treatment and management plans. This study aimed to enhance the efficient recognition of epilepsy types. Our methodology combines the CyTex algorithm with multichannel parallel convolution and RNNs neural networks for comprehensive analysis and classification. This integrated approach yielded notable results, allowing for the accurate differentiation of diverse epileptic events and ultimately achieving a recognition accuracy of 76.84%. Although these results are promising, it is acknowledged that there is still potential for further improvement in accuracy. This study provides valuable insights into epilepsy recognition and lays the foundation for future research in medical diagnosis and disease classification, although it does not represent a significant breakthrough in accuracy.