Resting-state SEEG-based brain network analysis for the detection of epileptic area

Wang, Aiping; Fan, Zhen; Zhang, Yuan; Wang, Junkongshuai; Zhang, Xueze; Wang, Pengchao; Mu, Wei; Zhan, Gege; Wang, Minjie; Zhang, Lihua; Gan, Zhongxue; Kang, Xiaoyang

doi:10.1016/j.jneumeth.2023.109839

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Through analysis of SEEG signals, we can more precisely capture the characteristics of these abnormal connectivity pattern, thereby furnishing a more dependable foundation for SOZ localization. Recent studies ( Xiao et al, 2021 ; Wang et al, 2023 ) have also demonstrated that SEEG shows potential in reflecting the distinction between epileptic and non-epileptic in brain networks and in identifying epileptic regions.…”

Section: Discussionmentioning

confidence: 99%

Epilepsy lesion localization method based on brain function network

Fang,

Li,

et al. 2024

Front. Hum. Neurosci.

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ObjectiveIn the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning.MethodTo overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification.ResultsThe proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others.ConclusionOur method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.

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Section: Discussionmentioning

confidence: 99%