Multivariate analysis and machine learning in pediatric epilepsy research: A literature review

Abstract

Due to brain immaturity, pediatric epilepsy has some unique challenges and opportunities in seizure management. Multivariate analysis and machine learning methods have been increasingly used in childhood epilepsy, mainly in seizure detection and prediction, epileptogenic lesion identification, and clinical outcome prediction. In order to provide an overview of this field, this paper reviewed such studies and found that these methods have made it possible to detect seizures on electroencephalogram (EEG) and detect lesions on imaging automatically. In addition, although seizure occurrence has been regarded as random or unpredictable for long, it has been found that seizures may occur non-randomly in complex patient-specific patterns. Preictal changes on EEG can be detected and distinguished from interictal activities by machine learning approaches, which makes it possible to predict seizure occurrence, but there are significant obstacles in seizure prediction, for example, sufficient clinical data and good machine learning algorithms are needed to identify complex seizure occurrence patterns. Further, outcome studies using multivariate analysis and machine learning methods have identified outcome predictors for seizure outcome prediction. To make these relatively new methods accurate and reliable, confirmatory studies are warranted and further research is needed to improve these methods. It is anticipated that multivariate analysis and machine learning will contribute more to identifying complex seizure patterns, epileptogenic lesions, and outcome predictors to improve seizure detection/prediction, lesion detection, and seizure outcome prediction, which will lead to better seizure control to prevent seizure-related accidents/injury in children with epilepsy, reduce mortality rate, improve quality of life and eventually set them free from seizures.