# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
###### Abstract
[9] developed a neonatal seizure detection algorithm based on autocorrelation analysis and SVM. From each epoch, a set of 21 features were extracted, defined in time, frequency, and time-frequency domains, as well as characteristics of autocorrelation analysis. These features were then fed into SVM classifiers for binary decision. Although the overall performance is satisfactory, there are still obvious drawbacks of this type of method. Accurate spatial annotation of seizure-containing EEG channels is indispensable for the proper training of classifiers, which requires additional inspection or preprocessing. In addition, conventional classifiers used in these methods have shown limitations in dealing with spatiotemporal features. In recent decades, neural network theory has advanced rapidly, leading to the emergence of a variety of network architectures that have demonstrated their potential as viable alternatives to traditional machine learning frameworks in several research fields [10; 11; 12; 13], including neonatal seizure detection. [PERSON] et al. [14] proposed a hybrid CNN-RF architecture that employed a convolutional neural network (CNN) for feature extraction, followed by a random forest (RF) for feature classification to enhance the overall detection performance. In the study by [PERSON] et al. [15], a deep learning framework based on the fully convolutional neural network (FCNN) was developed, which achieved end-to-end optimization of the feature extraction and classification. The experimental results demonstrate that the proposed FCNN system outperforms the two baseline systems based on SVM. [PERSON] et al. [16] proposed a 2D CNN framework for automated feature extraction and neonatal seizure detection. Three individual 2D CNN models were separately trained on a database annotated by three different experts. Subsequently, an ensemble model was created to predict seizure states by incorporating the opinions of the three CNN models. Compared to traditional machine learning frameworks, neural network frameworks have the advantage of holistically incorporating information from all channels and automatically optimizing the feature extraction and classification through the training process. However, the underlying interdependencies among different EEG channels have been overlooked. These interdependencies could provide critical insights into cerebral function and potentially improve the detection performance. In order to fully exploit the interdependencies of multi-channel EEG signals, graph-based methods have been developed in recent studies. Typically, these methods include two key components: graph generation and graph classification. In graph generation, the characteristics within each channel and the interdependencies between different channels are computed. Thus, a graph can be constructed by assigning the intra-channel features as graph nodes and the inter-channel features as graph edges. Graph classification involves graph-level feature extraction and feature classification, which can be integrated using graph convolutional neural networks (GCNNs). GCNN and its variants have distinct advantages in processing irregular data with graphical structures and have been employed in a number of EEG-based seizure detection studies. In the study by [PERSON] et al. [17], the raw EEG of each channel was used as the node features, and the Pearson correlation coefficients between channels were calculated as the adjacency matrix. The graphs were fed into the linear graph convolution network (LGCN) for further extraction and classification. [PERSON] et al. [18] proposed a seizure detection method based on the hierarchy graph convolution network (HGCN), where the node features of the graph were extracted from the time and frequency domains and the adjacency matrices of the graph were defined according to the longitudinal and transverse spatial relationships between channels, respectively. [PERSON] et al. [19] developed a neonatal seizure detection method based on the GCNN.