Machine Learning Insights on EEG Signals for Seizures in Human Brain

Seizures can affect not only human brain but also the human body in many ways. Its detection and categorization is one tough task but thanks to modern technologies like Machine Learning (ML) and Deep Learning (D L) tools this task is become easy and now the treatment has become faster than before. ML techniques like Support Vector Machine (SVM), Decision Tree, XGBoost, Random Forest classification will be used in this paper to compare the performance of the various ML algorithms so that we can quickly and efficiently detect the occurrence and categorise the seizure from the data which is live or recorded. ML has emerged as a powerful tool in the field of healthcare, particularly in the detection and prediction of neurological dis-orders. This paper explores the application of machine learning techniques to the analysis of Electroencephalogram (EEG) signals for the early detection of seizures in the human brain. Seizures, characterized by abnormal electrical activity in the brain, can have severe consequences if not detected and treated promptly. EEG signals provide valuable information about brain activity and are commonly used in the diagnosis of epilepsy and other seizure- related disorders. Machine learning algorithms, that are mentioned above have shown promise in automating the analysis of EEG data, enabling faster and more accurate seizure detection.