  Background: Wide-field calcium imaging (WFCI) with genetically encoded
calcium indicators allows for spatiotemporal recordings of neuronal activity in
mice. When applied to the study of sleep, WFCI data are manually scored into
the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG
and EMG recordings. However, this process is time-consuming, invasive and often
suffers from low inter- and intra-rater reliability. Therefore, an automated
sleep state classification method that operates on spatiotemporal WFCI data is
desired. New Method: A hybrid network architecture consisting of a
convolutional neural network (CNN) to extract spatial features of image frames
and a bidirectional long short-term memory network (BiLSTM) with attention
mechanism to identify temporal dependencies among different time points was
proposed to classify WFCI data into states of wakefulness, NREM and REM sleep.
Results: Sleep states were classified with an accuracy of 84% and Cohen's kappa
of 0.64. Gradient-weighted class activation maps revealed that the frontal
region of the cortex carries more importance when classifying WFCI data into
NREM sleep while posterior area contributes most to the identification of
wakefulness. The attention scores indicated that the proposed network focuses
on short- and long-range temporal dependency in a state-specific manner.
Comparison with Existing Method: On a 3-hour WFCI recording, the CNN-BiLSTM
achieved a kappa of 0.67, comparable to a kappa of 0.65 corresponding to the
human EEG/EMG-based scoring. Conclusions: The CNN-BiLSTM effectively classifies
sleep states from spatiotemporal WFCI data and will enable broader application
of WFCI in sleep.
