Long-term prognostic classification of West syndrome based on scalp EEG using phase-amplitude coupling

Abstract

West syndrome (WS), an infantile epileptic encephalopathy defined on the basis of epileptic spasms and hypsarrhythmia on the Electroencephalogram (EEG), is recognised to have a very poor long-term prognosis in terms of spasm control, freedom from other seizure types and developmental arrest.WS is an important clinical problem for patients and patients' families because of its poor developmental prognosis; however, the pathophysiological of WS have not been fully understood in spite of extensive work by many investigators. Accurate biomarkers of WS for the evaluating the effect and prognosis of treatment is needed.To predict the long-term prognosis of WS after the treatment, we used two deep learning models with the EEG in which High-frequency Oscillations(HFO) were appearing as input.The highest Micro-average accuracy rate was found to be 78\% , and Macro-average accuracy of 64\% was obtained from each subject.