An Enhanced Automated Epileptic Seizure Detection Using ANFIS, FFA and EPSO Algorithms

Objectives: Electroencephalogram (EEG) signal gives a viable perception about the neurological action of human brain that aids detection epilepsy. The objective this study is to build an accurate automated hybrid model for epileptic seizure detection. Methods: This work develops computer-aided diagnosis (CAD) machine learning which can spontaneously classify pre-ictal and ictal EEG signals. In proposed method two most effective nature inspired algorithms, Firefly algorithm (FFA) Efficient Particle Swarm Optimization (EPSO) are used determine optimum parameters Adaptive Neuro Fuzzy Inference System (ANFIS) network. Results: Compared FFA EPSO separately, composite (ANFIS+FFA+EPSO) optimization outperforms in all respects. technique achieved accuracy, specificity, sensitivity 99.87%, 98.71% 100% respectively. Conclusion: ANFIS-FFA-EPSO able enhance outcomes demand forecast hospital.