System and method for determining sleep and sleep stages of a person

• US 9,820,680 B2
• Filed: 05/15/2012
• Issued: 11/21/2017
• Est. Priority Date: 05/18/2011
• Status: Active Grant

• Current Assignee: V-Watch SA
• Original Assignee: V-Watch SA
• Inventors: Muzet, Alain Gilles
• Primary Examiner(s): LEVICKY, WILLIAM J
• Application Number:
  US14/117,690
• Publication Number:
  US 20140088378A1
• Time in Patent Office: 2,016 Days
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2560/0443 Modular apparatus
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• 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 5/4815 Sleep quality
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/4848 Monitoring or testing the effects of treatment, e.g. of medication
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence

Abstract

The present invention relates to a system and method for determining sleep, sleep stage and/or sleep stage transition of a person, including heart rate detecting means configured for detecting a heart rate of the person, movement detecting means configured for detecting a movement of a part of the body of the person, where the detected movement is caused by a skeletal muscle of the body, recording means configured for recording the detected heart rate and the detected movement of the part of the body, heart rate classifying means configured for classifying the recorded heart rate of the person into at least one heart rate class at least one heart rate variability class, movement classifying means configured for classifying the recorded movement into at least one movement class, and determining means configured for determining sleep, a sleep stage, a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class and the at least one movement class.

Claims

1. A system for determining sleep, a sleep stage, sleep stage transition, and/or a sleep event of a person, comprising:

a wearable device configured for detecting and recording a heart rate of the person and configured for detecting and recording a movement of a part of the body of the person, wherein the movement is caused by a skeletal muscle of the body;

an analysis device configured for classifying the recorded heart rate of the person into at least one heart rate class and at least one heart rate variability class, configured for classifying the recorded movement into a movement class comprising a large movement (LM), a small movement (SM) or a twitch (TM), based on a calculated intensity and/or duration of each movement, wherein the analysis device is capable of classifying the movement into any of the LM, SM, and TM, and configured for determining sleep, a sleep stage, a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class, the at least one heart rate variability class, and the movement class; and

a data connection configured for communicating data representing the recorded heart rate and the recorded movement from the wearable recording device to the analysis device, and wherein the analysis device is configured for classifying the recorded movement into the movement class based on the calculated intensity level of 1 to 2 counts per second, 3 to 5 counts per second, or 6 to 10 counts per second.

2. A system for determining sleep, a sleep stage, sleep stage transition, and/or a sleep event of a person, the system comprising:

heart rate detecting means configured for detecting a heart rate of the person;

movement detecting means configured for detecting a movement of a part of the body of the person caused by a skeletal muscle of the body and comprising movement sensing means configured for sensing an acceleration of the part of the body;

recording means configured for recording the detected heart rate and the detected movement of the part of the body;

movement calculating means configured for calculating, based on values of the sensed acceleration, at least an intensity and/or a duration of each movement of the part of the body;

heart rate classifying means configured for classifying the recorded heart rate of the person into at least one heart rate class and at least one heart rate variability class;

movement classifying means configured for classifying the recorded movement into a movement class comprising a large movement (LM), a small movement (SM), or a twitch movement (TM), based on the calculated intensity and/or the calculated duration of each movement, wherein the movement classifying means is capable of classifying the movement into any of the LM, SM, and TM; and

determining means configured for determining sleep, a sleep stage, a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class, the at least one heart rate variability class, and the at movement class, and wherein the movement classifying means are configured for classifying the recorded movement into the movement class based on the calculated intensity level of 1 to 2 counts per second, 3 to 5 counts per second, or 6 to 10 counts per second.

3. The system according to claim 2, further comprising:

heart rate calculating means configured for calculating a heart rate average, a variability value, a rhythm characteristic and/or a heart rate event or change from the recorded heart rate,

wherein the heart rate classifying means are configured for classifying the heart rate of the person based on the calculated heart rate average, variability value, rhythm characteristic and/or heart rate event or change from the recorded heart rate.

4. The system according to claim 2, wherein the determining means are further configured for identifying a specific combination of a heart rate class, a heart rate variability class, and a movement class within a specific time period, and

wherein the determining means are configured for determining sleep, the sleep stage, a sleep stage transition and/or a sleep event based on the identified specific combination.

5. The system according to claim 2, wherein the movement classifying means are further configured for classifying each LM, SM and/or TM at least into frequency classes and/or duration classes.

6. The system according to claim 2, further comprising:

environmental sensing means configured for sensing at least one environmental factor, wherein the recording means are further configured for recording the sensed at least one environmental factor; and

environmental classifying means configured for classifying at least some values of the at least one recorded environmental factor into at least one environmental class, wherein the determining means are further configured for determining sleep, the sleep stage, the sleep stage transition and/or the sleep event of the person based at least partially on the at least one environmental class.

7. The system according to claim 6, wherein the environmental sensing means are configured for sensing a noise level, an ambient temperature and/or an ambient light, the system further comprising:

environmental calculating means configured for calculating at least one average noise level and/or noise change and/or noise event based on the recorded noise level, and/or calculating at least one average ambient temperature level and/or change and/or variation based on the recorded ambient temperature, and/or calculating at least one ambient light level and/or change of ambient light level based on the recorded ambient light.

8. The system according to claim 6, wherein the determining means are further configured for determining a transition from waking to sleeping and/or a transition from one sleep stage to another and/or a transition from sleeping to waking and/or a direct causal effect of at least one recorded environmental factor on a sleep stage transition or a transition from sleeping to waking.

9. The system according to claim 6, further comprising:

evaluating means configured for evaluating a sleeping or waking state of the person based on the at least one heart rate class, the at least one heart rate variability class, and the at movement class, the at least one environmental class and/or any combination thereof.

10. The system of claim 2, wherein the at least one heart rate class comprises a heart rate average class.

11. The system according to claim 2, wherein the determining means are configured for identifying a combination of a heart rate class, a heart rate variability class, and a movement class within a time period, and for determining sleep, a sleep stage, a sleep stage transition, and/or a sleep event based on the identified combination.

12. A method for determining sleep, a sleep stage, a sleep stage transition, and/or a sleep event of a person, the method comprising:

detecting a heart rate of the person;

recording the detected heart rate;

detecting a movement of a part of the body of the person, wherein the movement is caused by a skeletal muscle of the body;

recording the detected movement;

classifying the recorded heart rate of the person into at least one heart rate class and at least one heart rate variability class;

classifying the recorded movement into a movement class comprising a large movement (LM), a small movement (SM), or a twitch movement (TM), based on a calculated intensity and/or the calculated duration of each movement, wherein the classifying includes classifying the movement into any of the LM, SM, and TM; and

determining sleep, a sleep stage, a sleep stage transition and/or a sleep event of the person based at least partially on the at least one heart rate class, the at least one heart rate variability class, and the movement class, and wherein classifying the recorded movement into the movement class is based on the calculated intensity level of 1 to 2 counts per second, 3 to 5 counts per second, or 6 to 10 counts per second.

13. The method according to claim 12, the method comprising:

identifying a specific combination of a heart rate class, a heart rate variability class and a movement class within a specific time period, and wherein determining comprises determining sleep, a sleep stage, a sleep stage transition, and/or a sleep event based on the identified specific combination.

14. The method according to claim 12, the method further comprising:

sensing at least one environmental factor;

recording the sensed at least one environmental factor;

classifying at least some values of the at least one recorded environmental factor into at least one environmental class; and

determining a sleeping or waking state of the person based at least partially on the at least one environmental class.

15. The method according to claim 14, the method further comprising:

determining a direct causal effect of at least one recorded environmental factor on a sleep stage transition or a transition from sleeping to waking based at least partially on the at least one environmental class.