  This paper proposes a novel framework for automatically capturing the
time-frequency nature of electroencephalogram (EEG) signals of human sleep
based on the authoritative sleep medicine guidance. The framework consists of
two parts: the first part extracts informative features by partitioning the
input EEG spectrograms into a sequence of time-frequency patches. The second
part is constituted by an attention-based architecture to efficiently search
for the correlation between partitioned time-frequency patches and defining
factors of sleep stages in parallel. The proposed pipeline is validated on the
Sleep Heart Health Study dataset with new state-of-the-art results for the
stages wake, N2, and N3, obtaining respective F1 scores of 0.93, 0.88, and
0.87, with only EEG signals used. The proposed method also has a high
inter-rater reliability of 0.80 kappa. We also visualize the correspondence
between sleep staging decisions and features extracted by the proposed method,
providing strong interpretability for our model.
