Title: A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging,
Abstract: Sleep plays a vital role in human health, both mental and physical. Sleep
disorders like sleep apnea are increasing in prevalence, with the rapid
increase in factors like obesity. Sleep apnea is most commonly treated with
Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is
no mechanism to monitor a patient's progress with CPAP. Accurate detection of
sleep stages from CPAP flow signal is crucial for such a mechanism. We propose,
for the first time, an automated sleep staging model based only on the flow
signal. Deep neural networks have recently shown high accuracy on sleep staging
by eliminating handcrafted features. However, these methods focus exclusively
on extracting informative features from the input signal, without paying much
attention to the dynamics of sleep stages in the output sequence. We propose an
end-to-end framework that uses a combination of deep convolution and recurrent
neural networks to extract high-level features from raw flow signal with a
structured output layer based on a conditional random field to model the
temporal transition structure of the sleep stages. We improve upon the previous
methods by 10% using our model, that can be augmented to the previous sleep
staging deep learning methods. We also show that our method can be used to
accurately track sleep metrics like sleep efficiency calculated from sleep
stages that can be deployed for monitoring the response of CPAP therapy on
sleep apnea patients. Apart from the technical contributions, we expect this
study to motivate new research questions in sleep science.