HNSleepNet: A Novel Hybrid Neural Network for Home Health-Care Automatic Sleep Staging with Raw Single-Channel EEG

Abstract

Proper scoring of sleep stages may offer more intuitive clinical information for assessing the sleep health and improving the diagnosis of sleep disorders in the smart home healthcare. It usually depends on an accurate analysis of the collected physiological signals, especially for the raw sleep Electroencephalogram (EEG). Most of the methods currently available just rely on the pre-processing or handcrafted features that need prior knowledge and preliminary analysis from the sleep experts and only a few of them take full advantage of the temporal information such as the inter-epoch dependency or transition rules among stages, which are more effective for identifying the differences among the sleep stages. In such cases, we proposed a novel hybrid neural network named HNSleepNet. It utilizes a two-branch CNN with multi-scale convolution kernels to capture the time-invariant features from the adjacent sleep EEG epochs both in time and frequency domains automatically, and attention-based residual encoder-decoder LSTM layers to learn the inter-epoch dependency and transition rules at the Sequence-wise level. After the two-step training, HNSleepNet can perform sequence-to-sequence automatic sleep staging with a raw single channel EEG in an end-to-end way. As the experimental results demonstrated, its performance achieved a better overall accuracy and macro F1-score (MASS: 88%, 0.85, Sleep-EDF: 87%-80%, 0.79-0.74) compared with the state-of-the-art approaches on various single-channels (F4-EOG (Left), Fpz-Cz and Pz-Oz) in two public datasets with different scoring standards (AASM and R&K), We hope this progress can make clinically practical value in promoting home sleep studies on various home health-care devices.