  We propose a generative model for single-channel EEG that incorporates the
constraints experts actively enforce during visual scoring. The framework takes
the form of a dynamic Bayesian network with depth in both the latent variables
and the observation likelihoods-while the hidden variables control the
durations, state transitions, and robustness, the observation architectures
parameterize Normal-Gamma distributions. The resulting model allows for time
series segmentation into local, reoccurring dynamical regimes by exploiting
probabilistic models and deep learning. Unlike typical detectors, our model
takes the raw data (up to resampling) without pre-processing (e.g., filtering,
windowing, thresholding) or post-processing (e.g., event merging). This not
only makes the model appealing to real-time applications, but it also yields
interpretable hyperparameters that are analogous to known clinical criteria. We
derive algorithms for exact, tractable inference as a special case of
Generalized Expectation Maximization via dynamic programming and
backpropagation. We validate the model on three public datasets and provide
support that more complex models are able to surpass state-of-the-art detectors
while being transparent, auditable, and generalizable.
