Jia Manhua, Liu Wenjian, Duan Junwei, Chen Long, Chen C L Philip, Wang Qun, Zhou Zhiguo
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Front Neurosci. 2022 Aug 1;16:967116. doi: 10.3389/fnins.2022.967116. eCollection 2022.
Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy patients especially those with refractory epilepsy. At present, a large number of deep learning algorithms such as Convolutional Neural Networks and Recurrent Neural Networks have been used to predict epileptic seizures and have obtained better performance than traditional machine learning methods. However, these methods usually transform the Electroencephalogram (EEG) signal into a Euclidean grid structure. The conversion suffers from loss of adjacent spatial information, which results in deep learning models requiring more storage and computational consumption in the process of information fusion after information extraction. This study proposes a general Graph Convolutional Networks (GCN) model architecture for predicting seizures to solve the problem of oversized seizure prediction models based on exploring the graph structure of EEG signals. As a graph classification task, the network architecture includes graph convolution layers that extract node features with one-hop neighbors, pooling layers that summarize abstract node features; and fully connected layers that implement classification, resulting in superior prediction performance and smaller network size. The experiment shows that the model has an average sensitivity of 96.51%, an average AUC of 0.92, and a model size of 15.5 k on 18 patients in the CHB-MIT scalp EEG dataset. Compared with traditional deep learning methods, which require a large number of parameters and computational effort and are demanding in terms of storage space and energy consumption, this method is more suitable for implementation on compact, low-power wearable devices as a standard process for building a generic low-consumption graph network model on similar biomedical signals. Furthermore, the edge features of graphs can be used to make a preliminary determination of locations and types of discharge, making it more clinically interpretable.
癫痫是一种慢性脑部疾病，会对患者的身心健康造成持续且严重的损害。对癫痫发作进行每日有效的预测对癫痫患者尤其是难治性癫痫患者至关重要。目前，大量深度学习算法，如卷积神经网络和循环神经网络，已被用于预测癫痫发作，并且比传统机器学习方法取得了更好的性能。然而，这些方法通常将脑电图（EEG）信号转换为欧几里得网格结构。这种转换会导致相邻空间信息丢失，这使得深度学习模型在信息提取后的信息融合过程中需要更多的存储和计算消耗。本研究提出了一种用于预测癫痫发作的通用图卷积网络（GCN）模型架构，以解决基于探索EEG信号图结构的癫痫发作预测模型过大的问题。作为一个图分类任务，该网络架构包括用一跳邻居提取节点特征的图卷积层、汇总抽象节点特征的池化层以及实现分类的全连接层，从而产生卓越的预测性能和更小的网络规模。实验表明，在CHB - MIT头皮脑电图数据集中的18名患者上，该模型的平均灵敏度为96.51%，平均AUC为0.92，模型大小为15.5 k。与需要大量参数和计算量且对存储空间和能量消耗要求较高的传统深度学习方法相比，该方法更适合在紧凑、低功耗的可穿戴设备上实现，作为在类似生物医学信号上构建通用低功耗图网络模型的标准流程。此外，图的边特征可用于对放电的位置和类型进行初步判定，使其在临床上更具可解释性。
