Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.

ABSTRACT: Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct f

SUBMITTER: Bogaarts JG 

PROVIDER: S-EPMC4958398 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature