In the crucial arena of neurological care, pre-seizure, and seizure diagnosis stand as imperative focal points. While existing literature has probed this area, it demands sustained exploration given the intricate nature of seizures and the profound implications of prompt diagnosis on patient prognosis. Greater insights and novel advancements in the field of epilepsy diagnosis and prognosis can significantly bolster patient health and potentially redefine treatment management. Deep learning models like long short-term memory networks (LSTM) show promise for sequential data analysis. However, their application to electroencephalogram (EEG) signals for seizure detection reveals challenges, especially in imbalanced datasets. In response, we develop a hybrid graph neural network, integrating Convolutional Neural Networks (CNN) and LSTM through optimized skip connections. These connections, combined with our optimized graph structure, ensure no loss of crucial temporal data. The CNN layer efficiently extracts spatial features from samples, while LSTM emphasizes the EEG signal's temporal nuances. A unique facet of our proposed architecture is its optimized structure which is obtained based on Bayesian optimization. It does not merely refine network parameters but also systematically determines the optimal neuron count, layering, and overall architecture of our graph neural network. Alongside our deep learning methodology, we conduct a dynamical analysis elucidating the intrinsic chaotic patterns of seizure neural EEG signals. We demonstrate that the phase space analysis provides valuable insight for wavelet time-scale pre-processing for pre-seizure and seizure diagnosis. The numerical and empirical results validate the performance of our novel and breakthrough approach. Also, the results are compared with outcomes obtained using LSTM in different conditions.

中文翻译：

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使用图卷积 LSTM 网络对混沌脑电图癫痫发作信号进行时空小波域神经影像诊断和预后

在神经病学护理的关键领域，癫痫发作前和癫痫诊断是当务之急的焦点。虽然现有文献已经探讨了这一领域，但鉴于癫痫发作的复杂性以及及时诊断对患者预后的深远影响，需要持续探索。癫痫诊断和预后领域的更深入见解和新进展可以显着促进患者健康，并有可能重新定义治疗管理。长短期记忆网络 (LSTM) 等深度学习模型显示出序列数据分析的前景。然而，它们在用于癫痫检测的脑电图（EEG）信号中的应用却面临着挑战，特别是在不平衡的数据集中。为此，我们开发了一种混合图神经网络，通过优化的跳跃连接集成了卷积神经网络 (CNN) 和 LSTM。这些连接与我们优化的图形结构相结合，确保不会丢失关键的时间数据。CNN 层有效地从样本中提取空间特征，而 LSTM 则强调 EEG 信号的时间细微差别。我们提出的架构的一个独特方面是其基于贝叶斯优化获得的优化结构。它不仅细化网络参数，还系统地确定图神经网络的最佳神经元数量、分层和整体架构。除了我们的深度学习方法之外，我们还进行了动力学分析，阐明了癫痫发作神经脑电图信号的内在混沌模式。我们证明，相空间分析为癫痫发作前和癫痫诊断的小波时间尺度预处理提供了有价值的见解。数值和实证结果验证了我们新颖且突破性方法的性能。此外，还将结果与在不同条件下使用 LSTM 获得的结果进行了比较。