Abstract: There is provided a method of generating outcomes for improving a sleep condition, comprising: iterating over sleep sessions: accessing previously generated instructions for treatment for improvement of the sleep condition for a recent sleep session obtained as a previous outcome of a machine learning model, accessing sleep-parameters computed for the recent sleep session, feeding the sleep-parameters and previously generated instructions into the machine learning model, and obtaining as an outcome of the machine learning model, instructions for presentation on a user interface indicating treatment for improvement of the sleep condition of the target individual for a new sleep session following the recent sleep session, wherein a new set of sleep-parameters are accessed for the new sleep session, for feeding into the machine learning model in combination with the instructions for the new sleep session, for obtaining another set of instructions for another sleep session following the new sleep session.

Abstract: There is provided a method of training a machine learning model for generating a sleep wellness score used for treatment of a sleep condition in a target individual, comprising: providing a baseline machine learning model with weights set to initial baseline values, accessing sleep-parameters computed for historical sleep sessions of the target individual, training the baseline machine learning model using the sleep-parameters for the historical sleep sessions of the target individual by adjusting the initial baseline values of the weights, to obtain a customized machine learning model, accessing sleep-parameters computed for previous sleep session(s) of the target individual, inputting the sleep-parameters computed for previous sleep session(s) into the customized machine learning model, and obtaining a sleep wellness score as an outcome of the customized machine learning model.

Abstract: There is provided a method of creating a presentation of a current awake-energy level, comprising: computing an estimate of initial awake-energy at a wake-up time of a target individual according to sleep-parameters computed according to a time interval that includes a previous night sleep, computing an estimate of a current awake-energy of the target individual indicative of a remaining amount of an estimated maximal amount of available awake-energy, computed according to the initial awake-energy and according to awake-parameters computed for an awake-time interval based on output of sensor(s) comprising: a physiological sensor that senses a physiological parameter, and/or an activity sensor that senses an activity, and outputting, via a user interface, an indication of the current awake-energy, wherein the computing the current awake-energy and the outputting are dynamically iterated over the awake-time interval while the target individual is awake.

Abstract: A method of analysis is disclosed. The method comprises receiving a non-ECG signal indicative of heart beats of a sleeping subject; extracting from the signal a series of inter-beat intervals (IBI); calculating at least one Poincare parameter characterizing a Poincare plot of the IBI series; and using the Poincare parameter(s) to determine a REM sleep of the sleeping subject. In some embodiments, sleep stages other than REM sleep and/or wake stages are determined.

Abstract: A method of analysis is disclosed. The method comprises receiving a non-ECG signal indicative of heart beats of a sleeping subject; extracting from the signal a series of inter-beat intervals (IBI); calculating at least one Poincare parameter characterizing a Poincare plot of the IBI series; and using the Poincare parameter(s) to determine a REM sleep of the sleeping subject. In some embodiments, sleep stages other than REM sleep and/or wake stages are determined.