Epileptic seizure prediction in intracranial EEG using critical nucleus based on phase transition. (November 2022)

• Record Type: Journal Article
• Authors: Zhong, Lisha; Wu, Jia; He, Shuling; Yi, Fangji; Zeng, Chen; Li, Xi; Li, Zhangyong; Huang, Zhiwei
• Is Part Of: Computer methods and programs in biomedicine. Volume 226(2022)
• Publication Date: 2022-11
• Subjects: Epilepsy -- iEEG -- Seizure prediction -- Critical nucleus -- Phase transition -- Spatiotemporal features; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107091
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Critical nucleus model of iEEG signals implemented for seizure prediction. Quantifying the critical transition of the brain activities in epileptic iEEG signals. A combination of temporal and spatial factors account for the majority seizure case. Achieved accuracy, sensitivity, and false-positive rate as 87.96%, 82.93%, and 0.11/h respectively in early warning seizure detection. Abstract: Background and objective: Epilepsy is the second most prevalent neurological disorder of brain activity, affecting about seventy million people, or nearly 1% of the world population. Epileptic seizures prediction is extremely important for improving the epileptic patients' life. This paper proposed a novel method to predict seizures by detecting the critical transition of brain activities with intracranial EEG (iEEG) signals. Methods: This article used three key measures of fluctuation, correlation and percolation to quantify pre-ictal states of epilepsy. Based on these measures, a ritical nucleus of iEEG signals was constructed and a composite index was introduced to detect the likelihood of impending seizures. In addition, we analyzed the dynamical mechanism of seizures at the tipping point from the perspective of spatial diffusion and temporal fluctuation. Results: The empirical results supported that the seizures are self-initiated via a critical transition in pre-ictal state and showed that the proposed model can achieve a good prediction performance. The average accuracy, sensitivity, specificity and false-positive rate (FPR) attain 87.96%, 82.93%, 89.33% and 0.11/h respectively. The results also suggest that the temporal and spatial factors have strong synergistic effect on triggering seizures. For those seizures consistent with critical transition, the predictive performance was greatly improved with sensitivity up to 96.88%. Conclusions: This article proposed a critical nucleus model combined with spatial and temporal features of iEEG signals capable of seizure prediction. The proposed model brings insight from phase transition into epileptic iEEG signals analysis and quantifies the transition of the state to predict epileptic seizures with high accuracy.