MSCNet-FS: development of intelligent epileptic seizure anticipation model by multi serial cascaded network with feature Specific using scalogram images of EEG signal.

The early stage of the Epileptic Seizure Anticipation (ESA) model plays a significant part in supplying accurate medical care. In this research work, a novel Multi Serial Cascaded Network with Feature Specific model is developed. The scalogram images are given as input to a developed model. Here, the Target Feature Selection is performed optimally using the Improved Fitness Value Index-Archimedes Optimization (IFVI-AO) Algorithm. Finally, the selections of accurate features are subjected to 'Bi-directional Long Short-Term Memory (Bi-LSTM)'. The implemented model is validated and provides timely results to detect epileptic seizure disorder.