WaveSleepNet: An Interpretable Network for Expert-Like Sleep Staging

Although deep learning algorithms have proven their efficiency in automatic sleep staging, their "black-box" nature has limited their clinical adoption. In this study, we propose WaveSleepNet, an interpretable neural network for sleep staging that reasons in a similar way to sleep clinical experts. In this network, we utilize the latent space representations generated during training to identify characteristic wave prototypes corresponding to different sleep stages. The feature representation of an input signal is segmented into patches within the latent space, each of which is compared against the learned wave prototypes. The proximity between these patches and the wave prototypes is quantified through scores, indicating the prototypes' presence and relative proportion within the signal. The scores serve as the decision-making criteria for final sleep staging. During training, an ensemble of loss functions is employed for the prototypes' diversity and robustness. Furthermore, the learned wave prototypes are visualized by analyzing occlusion sensitivity. The efficacy of WaveSleepNet is validated across three public datasets, achieving sleep staging performance that are on par with those of the state-of-the-art models. A detailed case study examining the decision-making process of WaveSleepNet demonstrates that it aligns closely with American Academy of Sleep Medicine (AASM) manual guidelines. Another case study systematically explained the misidentified reasons behind each sleep stage. WaveSleepNet's transparent process provides specialists with direct access to the physiological significance of the model's criteria, allowing for future validation, adoption and further enrichment by sleep clinical experts.