Method and system for contactless monitoring and evaluation of sleep states of a user

• US 20050148893A1
• Filed: 12/22/2004
• Published: 07/07/2005
• Est. Priority Date: 01/06/2004
• Status: Active Grant

• Current Assignee: Dale Julian Misczynski, Vladislav Bukhman
• Original Assignee: Dale Julian Misczynski, Vladislav Bukhman
• Inventors: Bukhman, Vladislav, Misczynski, Dale Julian
• Granted Patent: US 7,164,941 B2
• Time in Patent Office: Days
• US Class Current: 600/513
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/05 Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves impedance plethysmography A61B5/0295; measuring movement of the entire body or parts thereof A61B5/11; detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof A61B5/24
• CPC Class Codes: A61B 5/0809 by impedance pneumography
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/242 Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/6892 Mats
• CPC Class Codes: A61B 5/7257 using Fourier transforms

Abstract

The invention relates to a method and device for contactless monitoring and evaluation of sleep states of a user. The invention comprises a seamless continuous evaluation of cardiac activity variability and uses these data as an input for evaluation and quantitative assessment of sympathetic and parasympathetic activity of autonomic nervous system, which are reliable markers of physiological condition of a user. The device is embedded within two thin mattress pad layers, eliminating any direct contact between electrodes and a user'"'"'s body. The invention enables remote monitoring and evaluation of sleep states without involvement of trained professionals.

Claims

1. A method and system for contactless monitoring and evaluation of sleep states of the user comprising:

a) Contactless sleep monitoring device made in a form of the small mattress pad;

b) Electromagnetic inductance device embedded into said mattress pad and connected to a processor;

c) Passing an alternating current through excitation coil;

d) Measuring at least one induced current waveform derived from a receiver coil;

e) Processing said at least one induced current waveform associated with changes in the amplitude caused by eddy current losses in the torso region of the user;

f) Determining sleep stages based on the said changes in said eddy current.

2. The method of claim 1, wherein the act of processing said at least one induced current waveform comprises of evaluation of respiratory component and cardiac activity (CA) component (CA waveform) of said waveform.

3. The method of claim 2, wherein said respiratory component is smoothed using cubic spline approximation method.

4. The method of claim 3, wherein said respiratory component is removed.

5. The method of claim 2, wherein width of CA wave reflects the cardiac cycle that is defined as:

${\mathrm{HR}}_i=\frac{60}{W_i}*1000$

Where;

HR_i=heart rate of cardiac cycle i in beat/min;

W_i=width of wave i in ms

6. The method of claim 2, wherein magnitude of said CA wave reflects the stroke volume, while the area Q_i below said CA wave represents the cumulative CA index.

7. The method of claim 6, wherein said area Q_i can be determined by equation:

$\begin{array}{cc}{Q}_i={\int <br><br>}_a^b<br><br>f<br><br>\left(x\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>d<br><br>x& \left(1\right)\end{array}$

Where;

a=t_o;

b=t_e

8. The method of claim 7, wherein because of the discrete nature of analyzed signals, the area said Q_i of each said CA wave is calculated using a discrete representation of equation (1).

$\begin{array}{c}{Q}_i=\sum _{}^{}\end{array}$

j = 1 n ⁢

 

⁢

A j ( 2 )

Where;

Q_i=area of CA wave related to cardiac cycle i,

A_j=amplitude of sample j of CA wave of cardiac cycle i,

n=number of samples within cardiac cycle i.

9. The method of claim 6, wherein CA index of cardiac cycle i is equal:

CA_i=k*Q_in.u. 

 

(3)

Where;

k is a normalizing factor for converting calculated value of Q_i in the meaningful value;

Q_i=area of wave W_i;

n.u. stands for normalized unit.

10. The method of claim 1, wherein power spectrum analysis is used for determining of said sleep stages.

11. The method of claim 10, wherein Fast Fourier Transform (FFT) algorithm applied for calculation of low frequency (LF), reflecting both sympathetic and parasympathetic activities, and high frequency (HF), reflecting parasympathetic activity.

12. The method of claim 11, wherein total power (TP), said LF activity power (LP), and said HF activity power are calculated for the first 5 minutes of monitoring using said FFT algorithm.

13. The method of claim 10, wherein the wakefulness signature level Ws is calculated by:

$\begin{array}{cc}{W}_s=\frac{{\mathrm{LP}}_s}{{\mathrm{HP}}_s}& \left(4\right)\end{array}$

Where;

W_s=wakefulness signature value,

LP_s=signature sympathetic activity power,

HP_s=signature parasympathetic activity power.

14. The method of claim 10, wherein current said wakefulness level W_c is calculated in 5 minutes window with 30 second shift by:

$W_c=\frac{{\mathrm{LP}}_c}{{\mathrm{HP}}_c}$

Where;

W_c=current wakefulness value,

LP_c=current sympathetic activity power,

HP_c=current parasympathetic activity power.

15. The method of claim 10, wherein in order to determine said sleep stages said current wakefulness level is compared with said wakefulness signature level.

16. The method of claim 15, wherein if said W_c≦

0.75*W_s, (i.e. ratio of sympathetic activity to parasympathetic activity drops by equal or more then 25% during the last 5 minutes), then the user is considered in NREM sleep state.

17. The method of claim 10, wherein if said W_c increases by more than 15% and said LP doesn'"'"'t change and the previous record shows that the user was in NREM stage, then the user is considered in REM sleep state.

18. The method of claim 10, wherein if said W_c increases by more than 15% and LP increases and the previous record shows that the user was in NREM or REM sleep, then the user is considered in the after-sleep wakefulness state.

19. The system comprises the induction device embedded in said small mattress pad.

20. The embedded device of claim 19 comprising of 2 sensing coils for supplying an induced current from the subject without contacting the body;

excitation coil for induction magnetic field into the subject;

power amplifier providing AC current to the excitation coil;

amplifier which serves for reducing an effect of residue voltage;

lock-in amplifier for driving the excitation coil;

a PC for controlling the lock-in amplifier, preprocessing of the digital data of CA waveforms, storing intermediate and sleep history data, calculations, graphic display and communication.

21. The device of claim 20, wherein large size said excitation coil is made from flexible plastic-type material using metal lines instead of a wire core and providing a strong and relatively uniform induction field.