SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species
Fig 14
CNN-HMM for constraining state transitions.
The figure illustrates how the HMM is used on top of the CNN to enforce prediction sequences which adhere to physiological constraints. In particular, we disallow REM → NREM and WAKE → REM vigilance state transitions. The constraints are encoded through the transition probability matrix of the HMM, and the observation likelihoods are implicitly calculated by the CNN.