Detection of epileptic seizures through brain waves analysis using Machine Learning algorithms.

Abstract

Electroencephalogram(EEG) is an effective and non-invasive technique commonly used for monitoring brain activity. EEG readings are analyzed to determine changes in brain activity that may be useful for diagnosing neurological disorders and other seizure disorders. On the other hand, around 50 million people worldwide have epilepsy, making it one of the most common neurological diseases globally. The risk of premature death in people with epilepsy is up to three times higher than in the general population. Over the years, different researchers had been trying to detect seizures with different methods and with different approaches, but none algorithm has been fully implemented in the life of the people that have this disease, and for this reason, I developed a solution for this problem. The solution that I developed was to extract the information obtained by making a classification analysis using data acquired through the EEGs in a time-lapse of 1 second and once done, compare the results of the Machine Learning methods to find the best algorithms for solving the problem. The main objective of the algorithm is to find the most precise detection during epileptic seizures using public data, by extracting the temporal features from the electroencephalogram and with this learn the general structure of a seizure to make an effective detection in the less time possible.