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@article{177155,
        author = {Kushagra Jain and Manas Chaturvedi},
        title = {EpiSight: The Epilepsy Detection  System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5916-5920},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177155},
        abstract = {Epilepsy is a neurological disorder characterized by recurrent seizures, affecting millions of people worldwide. Timely and accurate seizure detection is crucial for effective treatment and patient management. Traditional methods rely on manual interpretation of electroencephalogram (EEG) signals, which can be time-consuming and error-prone. This research presents an automated epilepsy detection system utilizing machine learning techniques to classify seizure and non-seizure EEG signals. The proposed model is trained on publicly available EEG datasets, leveraging feature extraction and deep learning algorithms to improve accuracy. Experimental results demonstrate a high detection accuracy, making this system a promising tool for real- time epilepsy monitoring. Additional research explores the use of cloud-based storage for real-time seizure tracking and wearable integration.},
        keywords = {Epilepsy, EEG, Machine Learning, Seizure Detection, Signal Processing, Deep Learning, Neural Networks},
        month = {May},
        }