MVBNSleepNet: A Multi-View Brain Network-Based Convolutional Neural Network for Neonatal Sleep Staging

Abstract

Goal: To develop a high-performance and robust solution for neonatal sleep staging that incorporates spatial topological information and functional connectivity of the brain, which are often overlooked in existing approaches. Methods: We propose MVBNSleepNet, a multi-view brain network-based convolutional neural network. The framework integrates a multi-view brain network (MVBN) to characterize brain functional connectivity from linear temporal correlation, information-theoretic, and phase-dynamics perspectives, providing comprehensive spatial topological information. A masking mechanism is employed to enhance model robustness by simulating random dropout or low-quality signal conditions. Additionally, an attention mechanism focuses on key regions of the brain network and reveals structural brain connectivity during sleep, while a CNN module extracts spatial features from brain networks and classifies them into specific sleep stages. The model was validated on a clinical dataset of 64 neonatal EEG recordings using a leave-one-subject-out validation strategy. Results: MVBNSleepNet achieved an accuracy of 83.9% in the two-stage sleep task (sleep and wakefulness) and 76.4% in the three-stage task (active sleep, quiet sleep, and wakefulness), outperforming state-of-the-art methods. Conclusions: The proposed MVBNSleepNet provides a robust and accurate solution for neonatal sleep staging and offers valuable insights into the functional connectivity of the early neural system.