The early detection and diagnosis of diseases are critical for saving lives, and in the modern era, computer-aided technologies have become indispensable tools for radiologists and physicians. Electroencephalography (EEG) has long been utilized to analyze human behavior and detect brain-related diseases. Epilepsy, a neurological disorder characterized by recurrent seizures, presents significant diagnostic challenges. This paper explores the application of EEG in detecting epilepsy, emphasizing the integration of advanced computational techniques to enhance diagnostic accuracy. The study employs a hybrid classification approach that incorporates Dual-Tree Complex Wavelet Transform (DTCWT) for signal decomposition, followed by feature extraction and classification using Neural Networks and Adaptive Neuro-Fuzzy Inference System (ANFIS). This methodology aims to improve the sensitivity, specificity, and accuracy of epilepsy detection. Extensive experimentation on EEG datasets from Bern-Barcelona and CHB-MIT databases demonstrates the efficacy of the proposed hybrid system. The results show that incorporating DTCWT significantly outperforms traditional methods, achieving high detection rates and low rejection rates. Neural Networks are utilized for initial classification, while ANFIS assesses the severity of the identified epileptic signals. This approach ensures robust performance even with limited data, making it suitable for practical clinical applications. The study also highlights the importance of integrating various feature extraction techniques and classifiers to effectively capture the complex nature of EEG signals.Overall, this research provides a reliable, efficient, and scalable solution for the diagnosis of epilepsy, contributing significantly to the fields of biomedical engineering and neurology. The integration of advanced computational methods with EEG signal analysis not only enhances diagnostic capabilities but also opens avenues for further research into automated detection systems for other neurological disorders. This work underscores the potential of hybrid models in medical diagnostics, promoting the development of intelligent systems that can assist healthcare professionals in delivering timely and accurate diagnoses, ultimately improving patient outcomes and quality of life. Earlier detection and diagnosis of diseases on time saves human life. In the modern world, computer aided technologies are used by radiologists or physicians to detect diseases in an earlier manner. From the past decades, Electroencephalography (EEG) signals have been used to detect and analyze the behavior of human beings and to detect diseases which are related to the human brain. Epilepsy is a neurological condition characterized by recurrent episodes of sensory disruption, loss of consciousness, or convulsions, all of which belongs to neurological disorder. In this paper, the Epilepsy disease is discussed using statistical statements with the anatomy of the human brain. The applications of EEG signals in various fields are also discussed in this paper.

The early detection and diagnosis of diseases are critical for saving lives, and in the modern era, computer-aided technologies have become indispensable tools for radiologists and physicians