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@article{183175,
        author = {Vandana Govindan T. K and S Thomas George and M.S.P.Subathra and Hemanth Jaison},
        title = {A Literature Review on Automated Detection of Epileptic Seizures using Machine learning & Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {111-120},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183175},
        abstract = {The application of Artificial Intelligence (AI) techniques for the automated detection and classification of epileptic seizures is a rapidly evolving and highly promising field within neurology and biomedical engineering. This area aims to influence the power of AI, particularly machine learning (ML) and deep learning (DL), to analyze vast amounts of physiological data, primarily Electroencephalogram (EEG) signals, to identify and categorize seizure events with greater accuracy and efficiency than traditional manual methods. This paper presents a comprehensive review of the advances made in detection and classification of normal to epileptic seizures using ML and DL techniques. This review summarizes various conventional and deep machine learning algorithms used for automated seizure detection, discussed in the existing literatures. Also, the paper highlights about the epilepsy data bases available in public used by most of the papers for developing ML and DL algorithms. A general comparison on various deep learning techniques used for seizure detection is also presented.},
        keywords = {Epileptic seizure, Machine Learning, Deep Learning.},
        month = {},
        }