A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms have been used in the model's optimization to improve efficiency and accelerate convergence while extracting features from time and frequency domain EEG data. The framework has been considered on two public epilepsy datasets CHB-MIT and Siena. In the CHB-MIT dataset, comprising 24-channel EEG recordings from 12 patients, the model achieved an accuracy of 99.71%, a sensitivity of 99.60%, and a specificity of 99.61% for detecting epileptic spikes. Similarly, in the Siena dataset, which includes EEG data from 14 adult patients, the model demonstrated an average accuracy of 99.75%. Sensitivity averaged 99.94%, while specificity averaged 99.95%. The false positive rate (FPR) remained low at 0.0011, and the model obtained an average F1-score of 99.74%. For real-time hardware validation, the 1D-ResCNN model was deployed within the Typhoon HIL simulator, utilizing embedded C2000 microcontrollers. This hardware configuration allowed for immediate spike detection with minimal latency, ensuring reliable performance in real-time clinical applications. The findings imply that the suggested approach provides suitable for identifying epileptic spikes in real time for medical settings.