Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods usually adopt handcrafted features and manual parameter setting. The over-reliance on the expertise of specialists may lead to weak exploitation of features and low popularization of clinical application. This paper proposes a novel parameterless patient-specific method based on Fourier Neural Network (FNN), where the Fourier transform and backpropagation learning are synthesized to make the predictor more efficient and practical. The employment of FNN is the first attempt in the field of seizure prediction due to its automatic extraction of immanent spectra in epileptic signals. Despite the self-adaptive superiority of FNN, we introduce Convolutional Neural Network (CNN) to further improve its search capability in high-dimensional feature spaces. The study also develops a multi-layer module to estimate spectral power ratios of raw recordings, which optimizes the prediction by enhancing feature diversity. Based on these modules, this paper proposes a two-channel deep neural network: Fourier Ratio Convolutional Neural Network (FRCNN). To demonstrate the reliability of the model, we explain the mathematical meaning of hidden-layer neurons in FRCNN theoretically. This approach is evaluated on both intracranial and scalp EEG datasets. It shows that the predictor achieved a sensitivity of 91.2% and a false prediction rate (FPR) of 0.06h−1 across intracranial subjects and a sensitivity of 85.4% and an FPR of 0.14h−1 over scalp subjects. The results indicate that FRCNN enables the convenience of epilepsy treatments while preserving a high degree of precision. In the end, a detailed comparison with the previous methods demonstrates that FRCNN has achieved higher performance and generalization ability.

中文翻译：

---

使用卷积神经网络和频谱功率比进行癫痫发作预测的深度傅里叶神经网络

癫痫发作预测是耐药性癫痫最常用的治疗辅助策略之一。传统方法通常采用手工特征和手动参数设置。过度依赖专家的专业知识可能导致功能开发薄弱，临床应用普及率低。本文提出了一种基于傅里叶神经网络（FNN）的新型无参数患者特异性方法，其中傅里叶变换和反向传播学习相结合，使预测器更加高效和实用。FNN 的使用是癫痫预测领域的首次尝试，因为它可以自动提取癫痫信号中的内在光谱。尽管 FNN 具有自适应的优越性，我们引入卷积神经网络（CNN）以进一步提高其在高维特征空间中的搜索能力。该研究还开发了一个多层模块来估计原始记录的光谱功率比，通过增强特征多样性来优化预测。基于这些模块，本文提出了一种双通道深度神经网络：傅里叶比卷积神经网络（FRCNN）。为了证明模型的可靠性，我们从理论上解释了 FRCNN 中隐藏层神经元的数学意义。这种方法在颅内和头皮 EEG 数据集上进行了评估。结果表明，该预测器的灵敏度为 91.2%，错误预测率 (FPR) 为 0.06 它通过增强特征多样性来优化预测。基于这些模块，本文提出了一种双通道深度神经网络：傅里叶比卷积神经网络（FRCNN）。为了证明模型的可靠性，我们从理论上解释了 FRCNN 中隐藏层神经元的数学意义。这种方法在颅内和头皮 EEG 数据集上进行了评估。结果表明，该预测器的灵敏度为 91.2%，错误预测率 (FPR) 为 0.06 它通过增强特征多样性来优化预测。基于这些模块，本文提出了一种双通道深度神经网络：傅里叶比卷积神经网络（FRCNN）。为了证明模型的可靠性，我们从理论上解释了 FRCNN 中隐藏层神经元的数学意义。这种方法在颅内和头皮 EEG 数据集上进行了评估。结果表明，该预测器的灵敏度为 91.2%，错误预测率 (FPR) 为 0.06 这种方法在颅内和头皮 EEG 数据集上进行了评估。结果表明，该预测器的灵敏度为 91.2%，错误预测率 (FPR) 为 0.06 这种方法在颅内和头皮 EEG 数据集上进行了评估。结果表明，该预测器的灵敏度为 91.2%，错误预测率 (FPR) 为 0.06H-1跨颅内受试者，灵敏度为 85.4%，FPR 为 0.14H-1在头皮主题。结果表明，FRCNN 在保持高度精确性的同时，使癫痫治疗更加方便。最后，与以往方法的详细比较表明，FRCNN 取得了更高的性能和泛化能力。