Importance

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Abstract

Systems and methods for monitoring and staging sleep using cardiac and respiration information are disclosed. An exemplary system comprises a storage device to store a trained hybrid sleep detection and classification model that comprise a plurality of trained machine-learning models each trained to map input cardiac and respiratory data into one of multiple distinct model-specific awake or sleep classes, and a trained regression model to combine the model-specific awake or sleep classes to a composite awake or sleep classification. A sleep detector applies patient cardiac and respiratory information to the trained hybrid sleep detection and classification model to determine a composite awake or sleep classification for the patient. The composite awake or sleep classification is used to diagnose medical conditions including sleep apnea.

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First Claim