# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
###### Abstract  
Neonatal seizures are the most common clinical presentation of neurological dysfunction, requiring immediate attention and treatment. Manual detection of seizure events from continuous electroencephalogram (EEG) recordings is laborious and time-consuming. In this study, a novel graph-based method for automated neonatal seizure detection is proposed. The proposed method aims to improve the detection performance by the thorough representation of multi-channel EEG signals and the adaptive classification of multi-band graph representations. To achieve this, a band-wise feature extraction method is performed on the raw EEG to provide more detailed information for classification. In addition, a novel classification model, namely the multi-band graph neural network (MBCNN), is proposed, which utilizes the attention mechanism and can take full advantage of the multi-band graph representations to improve the classification performance. The proposed method is evaluated using the EEG recordings of 39 neonates from the Helsinki database. The MBGNN model gives an average area under the receiver operating characteristic curve (AUC) of 99.11%, an average positive predictive value (PPV) of 95.34%, and an average negative predictive value (NPV) of 96.66%. The experimental results show that the proposed method could fully exploit the multi-band EEG information and facilitate the classification of seizure/non-seizure EEG epochs, making it more appealing for patient-specific clinical applications. elecroencephalography; neonatal seizure detection; wavelet packet decomposition; graph neural network; attention mechanism +
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classified as \"seizure\" or \"non-seizure\" by comparing these clinical characteristics with predefined thresholds. For instance, [PERSON] et al. [3] proposed a method based on autocorrelation analysis that characterizes periodic activity in neonatal EEGs and distinguishes seizures from normal background activity. [PERSON] et al. [4] presented a method to detect neonatal seizure patterns using spectral analysis, where the rhythmicity, power and spectral stability characteristics of a given epoch are compared to those of a constantly updated background. [PERSON] et al. [5] developed a real-time neonatal seizure detection system. EEG waves with increased regularity, characterized by wave intervals, amplitudes and shapes, were detected as indicators of seizure discharges. [PERSON] et al. [6] identified two major neonatal seizure patterns and developed two separate detectors in parallel. The first detector was designed to detect high-frequency activity, while the second detector was designed to detect low-frequency activity, corresponding to the \"spike train seizures\" and \"oscillatory seizures\", respectively. Considering the specificity of neonatal EEG and the inter-individual variability, these methods are not sufficiently reliable for routine use in NICUs. More recently, data-driven methods based on machine learning techniques have been proposed for neonatal seizure detection. In these methods, features are mostly defined in the time, frequency, and information theory domains and fed into machine learning-based classifiers. The rules and thresholds for decision making could be learned from the data through supervised, semi-supervised or unsupervised algorithms. For instance, [PERSON] et al. [7] proposed a neonatal seizure detection system based on Gaussian mixture models (GMM) classifiers. A set of 55 features was extracted from each EEG epoch. For a better classification performance, principal component analysis (PCA) and linear discriminant analysis (LDA) were implemented to reduce the dimension of feature space. GMM classifiers were trained on the transformed features and provided the probability that a certain epoch belongs to the seizure or non-seizure class. In the study presented by [PERSON] et al. [8], the GMM classifiers in [7] were replaced by support vector machine (SVM) classifiers. [PERSON] et al.