A Review of Multivariate Analysis and Machine Learning in Pediatric Epilepsy Research

Pediatric epilepsy presents some unique obstacles and opportunities in seizure control due to brain immaturity. Seizure detection and prediction, epileptogenic lesion identification, and clinical outcome prediction are all areas where multivariate analysis and machine learning methods are increasingly being employed in childhood epilepsy. This paper reviewed such studies in order to provide an overview of the subject, and discovered that these methods have made it possible to detect seizures on an electroencephalogram (EEG) and detect lesions on imaging automatically. Furthermore, despite the fact that seizure occurrence has long been thought to be random or unexpected, it has been discovered that seizures can occur non-randomly in complex patient-specific situations. Machine learning approaches can detect and distinguish preictal changes on EEG from interictal activities, allowing seizure occurrence to be predicted. However, there are significant challenges in seizure prediction, such as the need for sufficient clinical data and good machine learning algorithms to identify complex seizure occurrence patterns. In addition, outcome studies utilising multivariate analysis and machine learning techniques have revealed seizure outcome factors.Confirmatory studies are needed to make these relatively new procedures accurate and dependable, and more research is needed to improve them. Multivariate analysis and machine learning are expected to contribute more to identifying complex seizure patterns, epileptogenic lesions, and outcome predictors to improve seizure detection/prediction, lesion detection, and seizure outcome prediction, resulting in better seizure control to prevent seizure-related accidents/injury in children with epilepsy, lower mortality rates, improve quality of life and eventually set them free from seizures.