Prediction of cognitive impairment in epilepsy with spike-and-wave activation in sleep based on EEG.

Background: Spike-and-wave activation in sleep (SWAS) may lead to irreversible cognitive impairment, progressing to epileptic encephalopathy (EE)-SWAS. However, existing EEG studies have not differentiated between SWAS patients based on their future cognitive outcomes. Objectives: This study aims to extract features from the sleep EEG of SWAS patients and develop a machine learning model to predict cognitive outcomes. Design: A retrospective study. Methods: A retrospective analysis was conducted on the sleep EEG obtained during the interictal period of 41 SWAS patients (15 with cognitive impairment). Cognitive impairment prediction models (support vector machine (SVM) and light gradient boosting machine (LightGBM)) were constructed based on spike-wave index (SWI) (the gold standard for diagnosing SWAS) and multidimensional EEG features, including functional connectivity (FC), minimum spanning tree (MST), and time-frequency-nonlinear (TFN) features, alongside a multidomain fusion (MDF) model integrating all feature sets. The performance of these EEG-based models was compared with the SWI model. Results: Models using the single SWI feature showed limited predictive capability (highest ACC: 0.756, AUC: 0.749). Conversely, multidimensional EEG models demonstrated superior performance. Specifically, the FC-based SVM model achieved the highest accuracy (0.873). Furthermore, MDF-based LightGBM models outperformed all single-feature-set models in overall discriminative capability (highest AUC: 0.934). These findings highlight the superiority of multidimensional EEG features over SWI for predicting cognitive outcomes in SWAS. Conclusion: The developed EEG-based machine learning models hold potential to assist in early cognitive prediction and treatment decisions for SWAS patients. However, given the current methodological limitations, further clinical verification is warranted.