WaveSleepNet: An Interpretable Network for Expert-like Sleep Staging
<|reference_start|>WaveSleepNet: An Interpretable Network for Expert-like Sleep Staging: Although deep learning algorithms have proven their efficiency in automatic sleep staging, the widespread skepticism about their "black-box" nature has limited its clinical acceptance. In this study, we propose WaveSleepNet, an interpretable neural network for sleep staging that reasons in a similar way to sleep 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 are served 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 analysing occlusion sensitivity. The efficacy of WaveSleepNet is validated across three public datasets, achieving sleep staging performance that are on par with the state-of-the-art models when several WaveSleepNets are combine into a larger network. A detailed case study examined the decision-making process of the WaveSleepNet which aligns closely with American Academy of Sleep Medicine (AASM) manual guidelines. Another case study systematically explained the misidentified reason behind each sleep stage. WaveSleepNet's transparent process provides specialists with direct access to the physiological significance of its criteria, allowing for future adaptation or enrichment by sleep experts.<|reference_end|>
@article{pei2024wavesleepnet:,
    title={WaveSleepNet: An Interpretable Network for Expert-like Sleep Staging},
    author={Yan Pei, Wei Luo},
    journal={arXiv preprint arXiv:2404.15342},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15342},
    primaryClass={eess.SP cs.LG}
}