Because of the development of the brain, paediatric epilepsy poses some unique challenges and opportunities in seizure control. In childhood epilepsy, multivariate analysis and machine learning methods are rapidly being used in seizure detection and prediction, epileptogenic lesion identification, and clinical outcome prediction. These methods have made it feasible to detect seizures on an electroencephalogram (EEG) and detect lesions on imaging automatically, according to this publication, which examined such studies to provide an overview of the subject. Furthermore, despite the fact that seizures have long been assumed to occur at random or without warning, it has been revealed that seizures can occur non-randomly in complex patient-specific conditions. Preictal variations on EEG can be detected and distinguished from interictal activities using machine learning techniques, allowing seizure occurrence to be predicted. Seizure prediction, on the other hand, has substantial obstacles, such as the need for appropriate clinical data and good machine learning algorithms to recognise complicated seizure occurrence patterns. Seizure outcome factors have also been discovered using multivariate analysis and machine learning techniques in outcome studies. More research is needed to improve these relatively new techniques and confirmatory studies are needed to make them accurate and dependable. 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, lower mortality rates, and improved quality of life in children with epilepsy.
Author (S) Details
Jing Zhang
Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA.
View Book :- https://stm.bookpi.org/NFMMR-V10/article/view/3507
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