BECTS Substate Classification by Granger Causality Density Based Support Vector Machine Model.

ABSTRACT: Objectives: To investigate the performance of substate classification of children with benign epilepsy with centrotemporal spikes (BECTS) by granger causality density (GCD) based support vector machine (SVM) model. Methods: Forty-two children with BECTS (21 females, 21 males; mean age, 8.6 ± 1.96 years) were classified into interictal epileptic discharges (IEDs; 11 females, 10 males) and non-IEDs (10 females, 11 males) substates depending on presence of central-temporal spikes or not. GCD was calculated on four metrics, including inflow, outflow, total-flow (inflow + outflow) and int-flow (inflow - outflow) connectivity. SVM classifier was applied to discriminate the two substates. Results: The Rolandic area, caudate, dorsal attention network, visual cortex, language networks, and cerebell

SUBMITTER: Dai XJ 

PROVIDER: S-EPMC6868120 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature