Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beat-to-beat interval.
To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage.
Furthermore, the model was also evaluated on three other databases.
We introduce a method for quantifying the inherent unpredictability of a continuous-valued time series via an extension of the differential Shannon entropy rate.