Abstract
Epilepsy is a brain disease that affects about 50 million people worldwide. It is characterized by excessive discharges in brain cells, leading the patient to have seizures, loss of consciousness, and alteration of the senses, among others. It is estimated that 5 million cases of this pathology are diagnosed annually. The most commonly used method to diagnose the disease is an electroencephalogram (EEG), which contains information about brain functions and is inexpensive. After the EEG is performed, it is observed by a professional to confirm or rule out the disease; however, this can be a delayed process, thus affecting a possible early treatment of the pathology. Those are why deep learning models have been used to create neural networks that can automatically classify this disease, thus facilitating the work of physicians and reducing time. This paper demonstrated that the deep learning model is more efficient than the machine learning model for pathology classification with an accuracy of 0.9767.
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Patiño-Claros, M.A., Holguin-García, S.A., Daza-Chica, A.E., Tabares-Soto, R., Bravo-Ortiz, M.A. (2023). Epileptic Seizure Prediction Methods Using Machine Learning and Deep Learning Models. In: Castillo Ossa, L.F., Isaza, G., Cardona, Ó., Castrillón, O.D., Corchado Rodriguez, J.M., De la Prieta Pintado, F. (eds) Trends in Sustainable Smart Cities and Territories . SSCT 2023. Lecture Notes in Networks and Systems, vol 732. Springer, Cham. https://doi.org/10.1007/978-3-031-36957-5_21
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