0950 Sound-based Sleep Staging at Home Using Smartphone via Deep Learning

Abstract

Abstract Introduction Daily sleep tracking at home is growing in demand as more and more people are aware of the significance of sleep. The objective of this study is to propose a sound-based sleep staging model based on deep learning that works well in home environments with recorded audio data from general smartphones. Methods Three different audio datasets were used. A labeled hospital dataset (PSG and audio, N=812) and an unlabeled home dataset (audio only, N=829) were used for training. A limited number of labeled sound data from home (PSG and audio, N=45) were used for testing. Our proposed HomeSleepNet has three components: (1) supervised learning using the labeled hospital data that trains the model to make correct predictions in hospital environments; (2) unsupervised domain adaptation (UDA), which used both the labeled hospital data and unlabeled home data, and transferred the sleep staging power from hospital domain to home domain by adversarial training; (3) unsupervised data augmentation for consistency training (UDC), which augmented hospital data by adding home noise and trained the model to make consistent predictions on original and augmented data. After all training, HomeSleepNet is expected to make robust sleep staging despite the home noise presence. Results HomeSleepNet achieved 76.2% accuracy on the sleep staging task in home environments for the 3-stage classification case (Wake, NREM, REM). Specifically, it correctly predicted 63.4% of wake, 83.6% of NREM sleep, and 64.9% of REM sleep. The contributions of UDA and UDC were demonstrated by the following results. The accuracy of the model trained without both was 69.2%. Either addition of UDA or UDC training to the model improved the performance, with increased accuracy of 69.3% for UDA and 73.5% for UDC. As expected, using both UDA and UDC (i.e., HomeSleepNet) achieved the best performance, with a 7% increase in accuracy compared to the model trained without both components. Conclusion To the best of our knowledge, this is the first sound-based sleep staging study conducted in home environments. Moreover, the sounds were recorded by commercial smartphones and not through professional devices. Our proposed model introduced a reliable and convenient method for daily sleep tracking at home. Support (if any)