MACHINE LEARNT MODEL TO DETECT REM SLEEP PERIODS USING A SPECTRAL ANALYSIS OF HEART RATE AND MOTION

摘要

The present disclosure relates to systems and methods for probabilistically estimating an individual's sleep stage based on spectral analyses of pulse rate and motion data. In one implementation, the method may include receiving signals from sensors worn by the individual, the signals including a photoplethysmographic (PPG) signal and an accelerometer signal; dividing the PPG signal into segments; determining a beat interval associated with each segment; resampling the set of beat intervals to generate an interval signal; and generating signal features based on the interval signal and the accelerometer signal, including a spectrogram of the interval signal. The method may further include determining a sleep stage for the individual by comparing the signal features to a sleep stage classifier included in a learning library. The sleep stage classifier may include one or more functions defining a likelihood that the individual is in the sleep stage based on the signal features.