Abstract
In the world, today more than 50 million people suffer from Epilepsy which is a chronic non-communicable disease that affects the human brain of all ages. In the human brain because of Seizure episodes, there is an excessive electrical discharge in a group of brain cells and it affects different parts of the brain as well it can lead to brain haemorrhage and sudden death. There are many forms of seizures, based on the frequency the symptoms and severity are different from person to person and also can vary from less than one per year to several per day. If symptoms are identified in the early stages, the risk of epilepsy severity could be controlled. The electrical impulses of the brain are recorded using EEG during the period of testing. The brain activity of the person changes if he/she has epileptic seizures which are known as epileptiform. In earlier research works, machine learning techniques and deep learning models are used for predicting epileptic seizures from Electroencephalograms (EEG) signals for a dataset of a maximum of 200 patients of random ages. In this work, we are proposing advanced Machine and Deep Learning Models for predicting epilepsy at the early stages w.r.t the comparison of previous research work on the same.
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Roopa, R., Pallavi, B., Jayadeva, T.S. (2023). Prognostic Detection of Epilepsy Using ML and Deep Learning Algorithms. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2746-3_66
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DOI: https://doi.org/10.1007/978-981-99-2746-3_66
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