Automatic and Quantitative Electroencephalographic Characterization of Drug-Resistant Epilepsy in Neonatal KCNQ2 Epileptic Encephalopathy

KCNQ2 epileptic encephalopathy is relatively common in early-onset neonatal epileptic encephalopathy and seizure severity varied widely, categorized as drug-sensitive epilepsy and drug-resistant epilepsy. However, in clinical practice, anti-seizure medicines need to be gradually adjusted based on seizure control which undoubtedly increases the economic burden of patients, so further positive anti-seizure regimens depend on whether seizure severity can be predicted in advance. In this paper, we proposed a reliable assessment to differentiate between drug-sensitive epilepsy and drug-resistant epilepsy caused by KCNQ2 pathogenic variants. Based on the electroencephalogram (EEG) and electrooculogram (EOG) signals, twenty-four classical temporal and spectral domain features were extracted and Gradient Boosting Decision Tree (GBDT) was employed to distinguish between patients with drug-sensitive epilepsy and drug-resistant epilepsy. In addition, we also systematically investigated the impact of channel combination and feature combination based on the forward stepwise selection strategy. By employing selected channels and features, the classification accuracy can reach 81.25% with a sensitivity of 57.14% and specificity of 100%. Compared with the state-of-the-art techniques, including the functional network, effective network, and common spatial patterns, the improvement of accuracy ranges from 37.5% to 56.25%, indicating the superiority of our proposed method. Overall, the proposed method may provide a promising tool to distinguish different seizure outcomes of KCNQ2 epileptic encephalopathy.

Automated summary

This study proposes a reliable assessment method to differentiate between drug-sensitive epilepsy and drug-resistant epilepsy caused by KCNQ2 pathogenic variants. By analyzing EEG and EOG signals, 24 features were extracted and the Gradient Boosting Decision Tree was used for classification. With selected channels and features, the classification accuracy can reach 81.25%, indicating the superiority of the proposed method.