Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient’s daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that combine with handcrafted features. The handcrafted features were selected by Max-Relevance and Min-Redundancy (mRMR) to obtain the optimal set of features. To extract the epileptic features from the fused multidimensional structure, we designed a P3D-BiConvLstm3D model, which is a combination of pseudo-3D convolutional neural network (P3DCNN) and bidirectional convolutional long short-term memory 3D (BiConvLstm3D). We also converted EEG signals into a multidimensional structure that fused spatial, manual features, and temporal information. The multidimensional structure is then fed into a P3DCNN to extract spatial and manual features and feature-to-feature dependencies, followed by a BiConvLstm3D input to explore temporal dependencies while preserving the spatial features, and finally, a channel attention mechanism is implemented to emphasize the more representative information in the multichannel output. The proposed has an average accuracy of 98.13%, an average sensitivity of 98.03%, an average precision of 98.30% and an average specificity of 98.23% for the CHB-MIT scalp EEG database. A comparison of the proposed model with other baseline methods was done to confirm the better performance of features through time–space nonlinear feature fusion. The results show that the proposed P3DCNN-BiConvLstm3D-Attention3D method for epilepsy prediction by time–space nonlinear feature fusion is effective.

中文翻译：

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使用伪三维 CNN 基于脑电图的癫痫发作预测

癫痫发作的特点是突然且不可预测，给患者的日常生活带来重大风险。准确可靠的癫痫发作预测系统可以在癫痫发作之前发出警报，并为患者和护理人员提供足够的时间来采取适当的措施。本研究提出了一种基于深度学习并结合手工特征的有效癫痫发作预测方法。通过最大相关性和最小冗余（mRMR）选择手工制作的特征以获得最佳特征集。为了从融合的多维结构中提取癫痫特征，我们设计了 P3D-BiConvLstm3D 模型，该模型是伪 3D 卷积神经网络（P3DCNN）和双向卷积长短期记忆 3D（BiConvLstm3D）的组合。我们还将脑电图信号转换为融合空间、手动特征和时间信息的多维结构。然后将多维结构输入 P3DCNN 以提取空间和手动特征以及特征与特征之间的依赖关系，然后通过 BiConvLstm3D 输入来探索时间依赖关系，同时保留空间特征，最后，实现通道注意机制以强调多通道输出中更具代表性的信息。该方案对于CHB-MIT头皮脑电图数据库的平均准确度为98.13%，平均敏感性为98.03%，平均精确度为98.30%，平均特异性为98.23%。将所提出的模型与其他基线方法进行比较，以确认通过时空非线性特征融合得到的特征具有更好的性能。结果表明，所提出的时空非线性特征融合预测癫痫的 P3DCNN-BiConvLstm3D-Attention3D 方法是有效的。