 This paper proposes a hybrid expert system for automatic electrical status epilepticus during sleep, ESES, quantification. The system takes morphological variations, individual variability, and medical knowledge into account when making decisions about ESES quantification. The system uses biogeography-based optimization, BBO, to determine the optimal parameters for each subject, which allows for more accurate and reliable quantification. The system was tested on a dataset collected from 20 subjects at Children's Hospital of Fudan University, Shanghai, China. The results showed that the proposed system outperformed existing methods, and the individualized system improved the performance of ESES quantification. This suggests that the proposed hybrid expert system is promising for supporting the diagnosis of ESES. This article was authored by Wei Zhou, Xin Zhao, Xinhua Wang, and others.