Machine Learning Classifiers to Detecting Epileptic Seizures

Examining the neuronal signals that brain neurons produce may help detect the serious chronic neurological disease known as epilepsy. Neurons are tightly connected to one another in order to transmit signals and communicate with internal organs. To identify these brain impulses, electroencephalogram (EEG) in addition electrocorticography (ECoG) media are usually employed. The signals are intricate, noisy, not stationary, and not linear, also they a plenty of data to create. As a consequence, finding knowledge about the brain and identifying seizures are tough tasks. Classifiers for machine learning can categorize EEG data, identify seizures, also showcase significant patterns without losing performance. As a consequence, several academics have developed a range of seizure detection techniques that make use of statistical traits then machine learning classifiers. The selection of appropriate classifiers and attributes presents the most challenges. This paper's goal is to provide a summary for the many different variations of these approaches over the past several years depending on the classification of statistical characteristics also “black-box” also “non-black-box” classifiers for machine learning. Cutting-edge techniques and concepts discussed will provide a meticulous clutch of seizure detection, enumeration, and future research prospects.