Temporal Neighborhood based Self-supervised Pre-training Model for Sleep Stages Classification

Human sleep must be carefully monitored due to its impact on health. Typically, time series data for sleep monitoring is multimodal, simultaneous, and continuous. Pre-training models based on self-supervised learning can identify internal data patterns without requiring external labelling. In this paper, we propose temporal neighbourhood-based self-supervised pre-training models for multi-modality sleep signals, including EEG, EOG, and Heart Rate Variability (HRV). Two neighbourhood formation approaches are based on 1) the stationarity or trend-stationarity of sleep signals; 2) the feature similarity of sleep signals to find neighbourhoods of a given query sleep signal fragment/window. Both time-domain and frequency-domain features have been extracted and processed. The proposed models will learn latent representations for time series via making binary predictions of whether a fragment/window of time series is a neighbour of the given query sleep signal fragment/window. Downstream sleep stage classifiers can incorporate the pre-training models for sleep stage classification. The experiments conducted on the large-scale multi-modality sleep monitoring data SHHS show that the proposed approaches outperform other baseline classification models, including CNN and LSTM.