METHOD AND APPARATUS FOR DETECTING A SLEEP STATE

1 . A method for detecting a sleep state by heart-rate and movement data, in which method neural network software is used for detecting a sleep state and in which the input data are movement data (move_count) and data derived from heart-rate data and data derived from inter-beat interval data and respiration data (RESP), comprising:

inputting time changing variables used as input data for the neural network software including:

move count

heart rate deviation (HRD)

heart rate difference (from minimum HR) (MHR)

mean absolute difference between successive RR-intervals (MAD)

respiration (Resp)

GRD, which is defined as GRD(t)=f_ma(MAX(0,hr(t)-min_hr)/MHRc(t)), where MHRc(t) is MHR(t) calculated with a chosen time window c,

cumulative sleep time, and

at least a portion of the variables (HRD, MHR, MAD, Resp, GRD) derived from the heart-rate and/or inter-beat interval data are further modified by 2-4 artificial average functions, which have the form f_ma(t)=y(t)=(c*y(t−1)+F_input(t))/(c+1), where c is 20-80 with a sample window of 5 s and F_input is the input function to be averaged.

2 . The method according to claim 1 , wherein the heart-rate data and night-time inter-beat interval data are generated with a PPG device.

3 . The method according to claim 1 , wherein in addition to the real-time detection of the method, a correction is calculated during a dynamic delay so that a time window of a chosen length, e.g. a five-minute time window, is applied to the incoming signal, whereupon an auxiliary logic corrects the incoming signal in accordance with previously defined rules, wherein the episodes constituting the bulk of one sleep state are first sought in the incoming signals, that part is selected in accordance with a main rule, e.g. 80% of the episodes, and, apart from previously defined exceptions, the whole episode is modified in accordance with the bulk of the sleep state.

4 . The method in accordance with claim 3 , wherein, as a special rule, AWAKE states are not modified in accordance with the bulk of the state.

5 . The method according to claim 1 , wherein a calculation of night-time averages of the heart-rate variability variables occurs in the method in such a way that

two neural models are implemented as follows:

during the measurement a first night, a less accurate neural model is used for the detection of a sleep state and an average of the heart-rate variability variables is stored in real time, e.g. the first 4 h of the night are a sufficient sample for the variables to stabilize, after which it is possible to switch to using a neural model that utilizes these averages.

6 . The method according to claim 5 , wherein values of the heart-rate variability variables are distributed in the calculation by the average night-time heart-rate variability.

7 . The method according to claim 5 , wherein the method includes two different neural models for the detection, of which a first, less accurate, model is used when the night-time background parameters have not yet been calculated and the subsequent model is used when the background parameters have been calculated.

8 . A device for detecting a sleep state by heart-rate and movement data, the device comprising:

a software-operating system, including a CPU, RAM and ROM memory, and input unit for receiving heart-rate data and movement data, an output device and neural network software, wherein the device includes software configured to detect a sleep state by the method by:

inputting time changing variables used as input data for the neural network software including:

move count

heart rate deviation (HRD)

heart rate difference (from minimum HR) (MHR)

mean absolute difference between successive RR-intervals (MAD)

respiration (Resp)

GRD, which is defined as GRD(t)=f_ma(MAX(0,hr(t)-min_hr)/MHRc(t)), where MHRc(t) is MHR(t) calculated with a chosen time window c,

cumulative sleep time, and

at least a portion of the variables (HRD, MHR, MAD, Resp, GRD) derived from the heart-rate and/or inter-beat interval data are further modified by 2-4 artificial average functions, which have the form f_ma(t)=y(t)=(c*y(t−1)+F_input(t))/(c+1), where c is 20-80 with a sample window of 5 s and F_input is the input function to be averaged.

9 . The device according to claim 8 , wherein the device includes a PPG wrist device as its only source of heart-rate data.

10 . The device according to claim 8 , wherein the device includes an ECG heart-rate monitor as its only source of heart-rate data.