Coherence Matrix Based Early Infantile Epileptic Encephalopathy Analysis with ResNet.

EIEE syndrome, known as early infantile epileptic encephalopathy, is considered to be the earliest onset form of age-dependent epileptic encephalopathy. The main manifestations are tonic-spasmodic seizures in early infancy, accompanied by burst suppressive electroencephalogram (EEG) patterns and severe psychomotor disturbances, with structural brain lesions in some cases. Specific to EIEE syndrome, this paper presents a comprehensive analysis of EEG features at three different periods: pre-seizure, seizure and post-seizure. Coherent features are extracted to characterize EEG signals in EIEE syndrome, and Kruskal-Wallis H Test and Gradient-weighted Class Activation Mapping (Grad-CAM) are used to investigate and visualize the significance of features in different frequency band for distinguishing the three stages. The study found that activity synchrony between temporal and central regions decreased significantly in the γ band during seizures. And the coherence feature in the γ band combined with the ResNet18-based seizure detection model achieved an accuracy of 91.86 γ band can be considered as a biomarker of seizure cycle changes in EIEE syndrome.