发表机构
Wuhan Textile University; Michigan State University（武汉纺织大学; 密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对EEG预测中忽视动态与几何信息的问题，提出混沌动力学调控拓扑学习框架，融合Lorenz振荡器与持久拉普拉斯，在CHB-MIT上实现患者特异性发作前期节点离线识别。
AI 中文摘要
癫痫发作源于脑网络内复杂的非线性相互作用，然而，由于神经动力学的非平稳性和异质性，可靠的脑电图（EEG）预测仍然具有挑战性。现有方法通常将EEG数据视为静态或弱时间依赖的快照，忽视了内在动力学，并缺乏捕捉致痫灶层级化、局部化演化的几何敏感性。为解决这些局限性，我们提出了一种离线的、患者特异性的混沌动力学调控拓扑学习（CDRTL）评估方法，用于区分发作前期与发作间期EEG状态。该框架统一了混沌动力学、多尺度代数拓扑和局部网络分化。具体而言，我们根据相关性强度将EEG信号划分为离散的功能性子网，以捕捉大脑的多尺度连接性。通过将每个节点建模为Lorenz振荡器，我们将潜在的混沌动力学嵌入网络架构中。然后，我们应用持久拉普拉斯算子，通过调和与非调和谱分析同时提取拓扑不变量和几何形状演化。此外，一种节点移除的拓扑分化策略隔离了局部神经贡献。我们在CHB-MIT数据库上评估了该框架，使用平衡的发作前期和发作间期标签，并在每个患者内进行分层通道级交叉验证。结果支持在固定的患者特异性网络中对发作前期和发作间期通道级节点进行离线区分。由于表示是从完整网络（包括留出的未标记节点）构建的，然后进行交叉验证，所报告的性能特定于这种转导设置，并不建立对未见EEG窗口、癫痫发作或患者的泛化能力。
英文摘要
Epileptic seizures arise from complex, nonlinear interactions within brain networks, yet reliable electroencephalographic (EEG) prediction remains challenging due to the nonstationary and heterogeneous nature of neural dynamics. Existing methods typically analyze EEG data as static or weakly time-dependent snapshots, overlooking the intrinsic dynamics and lacking the geometric sensitivity to capture the hierarchical, localized evolution of the epileptogenic zone. To address these limitations, we propose an offline, patient-specific evaluation of chaotic dynamics-regulated topological learning (CDRTL) for distinguishing preictal from interictal EEG states. This framework unifies chaotic dynamics, multiscale algebraic topology, and local network differentiation. Specifically, we partition EEG signals into discrete functional subnets based on correlation strengths, capturing the multi-scale connectivity of the brain. By modeling each node as a Lorenz oscillator, we embed the underlying chaotic dynamics into the network architecture. We then apply the persistent Laplacian to simultaneously extract topological invariants and geometric shape evolution through harmonic and non-harmonic spectral analysis. Additionally, a node-removal topological differentiation strategy isolates localized neural contributions. Our framework was evaluated on the CHB-MIT database using balanced preictal and interictal labels and stratified channel-level cross-validation within each patient. The results support offline discrimination of preictal and interictal channel-level nodes within fixed patient-specific networks. Because representations are constructed from the complete network, including held-out unlabeled nodes, before cross-validation, the reported performance is specific to this transductive setting and does not establish generalization to unseen EEG windows, seizures, or patients.
Comments23 pages, 5 figures. Code available at https://github.com/Wzhgeek/CDRTL
