A Hybrid Expert System for Individualized Quantification of Electrical Status Epilepticus During Sleep Using Biogeography-Based Optimization

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2022, Vol.30, p.1920-1930
• Main Authors: Zhou, Wei, Zhao, Xian, Wang, Xinhua, Zhou, Yuanfeng, Wang, Yalin, Meng, Long, Fan, Jiahao, Shen, Ning, Zhou, Shuizhen, Chen, Wei, Chen, Chen
• Format: Article
• Language: English
• Subjects: Biogeography; Biogeography-based optimization; Brain modeling; Decision analysis; Decision making; Detectors; Diagnosis; EEG; electrical status epilepticus during sleep; Electroencephalography; Encephalopathy; Epilepsy; Expert systems; Feature extraction; Firing pattern; Hybrid systems; Morphology; Optimization; Reliability analysis; Sleep
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2022.3186942

Electrical status epilepticus during sleep (ESES) is an epileptic encephalopathy in children with complex clinical manifestations. It is accompanied by specific electroencephalography (EEG) patterns of continuous spike and slow-waves. Quantifying such EEG patterns is critical to the diagnosis of ESES. While most of the existing automatic ESES quantification systems ignore the morphological variations of the signal as well as the individual variability among subjects. To address these issues, this paper presents a hybrid expert system that dedicates to mimicking the decision-making process of clinicians in ESES quantification by taking the morphological variations, individual variability, and medical knowledge into consideration. The proposed hybrid system not only offers a general scheme that could propel a semi-auto morphology analysis-based expert decision model to a fully automated ESES quantification with biogeography-based optimization (BBO), but also proposes a more precise individualized quantification system to involve the personalized characteristics by adopting an individualized parameters-selection framework. The feasibility and reliability of the proposed method are evaluated on a clinical dataset collected from twenty subjects at Children's Hospital of Fudan University, Shanghai, China. The estimation error for the individualized quantitative descriptor ESES is 0-4.32% and the average estimation error is 0.95% for all subjects. Experimental results show the presented system outperforms existing works and the individualized system significantly improves the performance of ESES quantification. The favorable results indicate that the proposed hybrid expert system for automatic ESES quantification is promising to support the diagnosis of ESES.