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Sleep plays a vital role in human health, both mental and physical. Sleepdisorders like sleep apnea are increasing in prevalence, with the rapidincrease in factors like obesity. Sleep apnea is most commonly treated withContinuous Positive Air Pressure (CPAP) therapy. Presently, however, there isno mechanism to monitor a patient's progress with CPAP. Accurate detection ofsleep 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 flowsignal. Deep neural networks have recently shown high accuracy on sleep stagingby eliminating handcrafted features. However, these methods focus exclusivelyon extracting informative features from the input signal, without paying muchattention to the dynamics of sleep stages in the output sequence. We propose anend-to-end framework that uses a combination of deep convolution and recurrentneural networks to extract high-level features from raw flow signal with astructured output layer based on a conditional random field to model thetemporal transition structure of the sleep stages. We improve upon the previousmethods by 10% using our model, that can be augmented to the previous sleepstaging deep learning methods. We also show that our method can be used toaccurately track sleep metrics like sleep efficiency calculated from sleepstages that can be deployed for monitoring the response of CPAP therapy onsleep apnea patients. Apart from the technical contributions, we expect thisstudy to motivate new research questions in sleep science.

A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging