Epileptic seizure prediction in intracranial EEG using critical nucleus based on phase transition

Zhong, Lisha; Wu, Jia; He, Songnian; Yi, Fangji; Zeng, Chen; Li, Xi; Li, Zhangyong; Huang, Zhiwei

doi:10.1016/j.cmpb.2022.107091

Cited by 1 publication

(1 citation statement)

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“…As the second most common disease after stroke, epilepsy affects approximately 70 million people worldwide, which is nearly 1% of the global population, and approximately 80% in developing countries according to the World Health Organization. The sudden and recurring seizures are catastrophic for patients, easily resulting in loss of consciousness, injury and even death by accidents ( Lenkov et al, 2013 ; Zhong et al, 2022c ). Therefore, reliable seizure prediction is prime important, as it can greatly improve the quality of patients lives.…”

Section: Introductionmentioning

confidence: 99%

Epileptic prediction using spatiotemporal information combined with optimal features strategy on EEG

Zhong

Wan

et al. 2023

Front. Neurosci.

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ObjectiveEpilepsy is the second most common brain neurological disease after stroke, which has the characteristics of sudden and recurrence. Seizure prediction is seriously important for improving the quality of patients’ lives.MethodsFrom the perspective of multiple dimensions including time-frequency, entropy and brain network, this paper proposed a novel approach by constructing the optimal spatiotemporal feature set to predict seizures. Based on strong independence and large information capabilities, the two-dimensional feature screening algorithm is performed to eliminate unnecessary redundant features. In order to verify the effectiveness of the optimal feature set, support vector machine (SVM) was used to classify the preictal and interictal states on both the Kaggle intracranial EEG and CHB-MIT scalp EEG dataset.ResultsThis model achieved an average accuracy of 98.01%, AUC of 0.96, F-Score of 98.3% and FPR of 0.0383/h on the Kaggle dataset; On the CHB-MIT dataset, the average accuracy, AUC, F-score and FPR were 95.93%, 0.92, 94.97% and 0.0473/h, respectively. Further ablation experiments have confirmed that the temporal and spatial features fusion has better performance than the individual temporal or spatial features.ConclusionCompared to the state-of-the-art methods, our approach outperforms most of these existing techniques. The results show that our approach can effectively extract the spatiotemporal information of epileptic EEG signals to predict epileptic seizures with high performance.

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Section: Introductionmentioning

confidence: 99%