A comprehensive study of machine learning-based methods to predict epileptic seizures

People with epilepsy have many difficulties as a result of this complicated brain condition, which is typified by frequent convulsions. Symptoms of these seizures include bizarre behaviors, odd sensations, and in extreme cases, loss of awareness. These seizures appear as episodes of aberrant electrical impulses in the central nervous system. Successful epilepsy management depends on early seizure detection and identification, which allows for appropriate intervention to minimize risks and improve patient outcomes. Two major reasons have contributed to the extraordinary advancements in the area of epilepsy investigations in the last few years: the explosive development of machine learning techniques and the decreasing cost of non-invasive electroencephalography (EEG) apparatus. The availability of low-cost EEG equipment has made it easier to gather information on brain activity, which has opened up new avenues for monitoring and analyzing episodes of epileptic seizures away from conventional medical settings. The abundance of data and the advancement of machine learning methods have created new opportunities for the early identification and forecasting of seizures. Machine learning algorithms can predict seizures based on EEG data, providing patients with epilepsy with more control and informed decision-making. This paper offers a current review of current methods for treating epileptic seizures. The feature extraction techniques and classification algorithms receive particular focus. The most popular EEG datasets and their accessibility are listed. The approaches that are examined range from those that use more established machine learning techniques, such as naive Bayes models, Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), to those that take advantage of more recent deep learning techniques, like (Long-Short Term Memory, or LSTM), and deep Convolutional-Neural-Networks (CNN).