Automatic Sleep Staging with a Convolutional Bidirectional Gated Recurrent Unit Model Using Non-Wearable Device Data

Sleep quality is paramount for human physical and mental health, where accurate sleep staging is crucial for its assessment. While polysomnography (PSG) offers high accuracy, its complexity and inconvenience hinder widespread practicality. In contrast, non-wearable devices offer simplicity and comfort but suffer from limited monitoring capabilities, allowing only indirect assessment of sleep status, thereby posing challenges for predictive methods. To overcome these limitations, this paper introduces an automatic sleep staging model leveraging a convolutional-bidirectional gated recurrent neural network. By monitoring a select set of physiological parameters through non-wearable devices, the model achieves sleep staging accuracy approaching that of PSG, while significantly enhancing user comfort. Despite the experimental results not yet being fully optimized, the method's application potential and promising future directions are well-established.