Exploring the propagation pathway in individual patients with epilepsy: A stepwise effective connection approach

Sun, Jie; Niu, Yan; Wang, Chunhong; Dong, Yanqing; Wang, Bin; Wei, Jing; Xiang, Jie; Ma, Jiuhong

doi:10.1016/j.bspc.2023.105811

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2023.105811

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Exploring the propagation pathway in individual patients with epilepsy: A stepwise effective connection approach

Jie Sun,

Yan Niu,

Chunhong Wang

et al.

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2024

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Cited by 2 publications

References 41 publications

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Virtual resection evaluation based on sEEG propagation network for drug-resistant epilepsy

Sun,

Niu,

Dong

et al. 2024

Sci Rep

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Drug-resistant epilepsy with frequent seizures are considered to undergo surgery to become seizure-free, but seizure-free rates have not dramatically improved, partly due to imprecise intervention locations. To address this clinical need, we construct effective connectivity to reveal epilepsy brain dynamics. Based on the propagation path captured by the high order effective connectivity, calculate the control centrality evaluation scheme of the excised area. We used three datasets: simulation dataset, clinical dataset, and public dataset. The epileptogenic propagation network was quantified by calculating high-order effective connection to obtain accurate propagation path, based on this, combined with the outdegree index for virtual resection. By removing electrodes and recalculating control centrality, we quantify each electrode or region’s control centrality to evaluate the virtual resection scheme. Three datasets obtained consistent results. We track the accurate propagation path and find the obvious inflection points occurring during the excision process. The minimum intervention targets were obtained by comparing different schemes without recurrence. The clinical data with multiple seizures found that after resection, the brain reaches a stable state and is less likely to continue spreading. By quantitative analysis of control centrality to evaluate the possible excision scheme, finally we obtain the best intervention area for epilepsy, which assist in developing surgical plans. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-77216-w.

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Virtual resection evaluation based on sEEG propagation network for drug-resistant epilepsy

Sun,

Niu,

Dong

et al. 2024

Sci Rep

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Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy

Sun,

Xiang,

Dong

et al. 2024

Entropy

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Drug-resistant epilepsy is frequent, persistent, and brings a heavy economic burden to patients and their families. Traditional epilepsy detection methods ignore the causal relationship of seizures and focus on a single time or spatial dimension, and the effect varies greatly in different patients. Therefore, it is necessary to research accurate automatic detection technology of epilepsy in different patients. We propose a causal-spatio-temporal graph attention network (CSTGAT), which uses transfer entropy (TE) to construct a causal graph between multiple channels, combining graph attention network (GAT) and bi-directional long short-term memory (BiLSTM) to capture temporal dynamic correlation and spatial topological structure information. The accuracy, specificity, and sensitivity of the SWEZ dataset were 97.24%, 97.92%, and 98.11%. The accuracy of the private dataset reached 98.55%. The effectiveness of each module was proven through ablation experiments and the impact of different network construction methods was compared. The experimental results indicate that the causal relationship network constructed by TE could accurately capture the information flow of epileptic seizures, and GAT and BiLSTM could capture spatiotemporal dynamic correlations. This model accurately captures causal relationships and spatiotemporal correlations on two datasets, and it overcomes the variability of epileptic seizures in different patients, which may contribute to clinical surgical planning.

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Exploring the propagation pathway in individual patients with epilepsy: A stepwise effective connection approach

Cited by 2 publications

References 41 publications

Virtual resection evaluation based on sEEG propagation network for drug-resistant epilepsy

Virtual resection evaluation based on sEEG propagation network for drug-resistant epilepsy

Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy

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