METHODS AND SYSTEM OF DETERMINING CARDIO-RESPIRATORY PARAMETERS

• US 20110021928A1
• Filed: 07/14/2010
• Published: 01/27/2011
• Est. Priority Date: 07/23/2009
• Status: Abandoned Application

• Current Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Original Assignee: Board of Trustees of the Leland Stanford Junior University (Stanford Management Co.)
• Inventors: Inan, Omer T., Giovangrandi, Laurent, Pandia, Keya R.
• Application Number:
  US12/836,572
• Publication Number:
  US 20110021928A1
• Time in Patent Office: Days
• US Class Current: 600/484
• 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 5/0507 using microwaves or terahertz waves
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Embodiments of the present invention provide noninvasive methods and systems of determining and monitoring an individual'"'"'s respiration pattern, respiration rate, other cardio-respiratory parameters or variations thereof.

Claims

1. A method of determining an individual'"'"'s respiration pattern, respiration rate, other cardio-respiratory parameters or variations thereof, the method comprising

sensing

a) mechanical movements of the individual'"'"'s chest;

and/or

b) acoustic waves generated by the individual'"'"'s heart beats;

processing said mechanical movements and/or acoustic waves to obtain one or more respiration-dependent parameters for subsequent computational analysis of the individual'"'"'s cardio-respiratory parameters, wherein

one respiration-dependent parameter is a variation in S1-S2 intervals between two consecutive heart beats and another respiration-dependent parameter is a variation in heart sound amplitude.

2. The method of claim 1, wherein the computational analysis comprises

estimating S1-S2 interval variations between a beat and its preceding beat;

wherein

beats include both first (S1) and second (S2) heart sounds;

quantifying the similarity between the preceding beat and versions of the beat;

identifying maximum similarity throughout the versions and assigning a corresponding S1-S2 interval variation to the beat based on identified version.

3. The method of claim 2, wherein the heart beat is detected by its first heart sound S1, and wherein assessing a degree of similarity only includes the second sound S2.

4. The method of claim 2, wherein the heart beat is detected by its second heart sound S2, and wherein assessing a degree of similarity only includes the first sound S1.

5. The method of claim 1, wherein an additional respiration-dependent parameter is a variation in S1-S1 intervals.

6. The method of claim 1, wherein an additional respiration-dependent parameter is chest wall motion.

7. The method of claim 1, wherein the computational analysis is carried out with a combined plurality of respiration-dependent parameters.

8. The method of claim 1, wherein the computational analysis is carried out with one respiration-dependent parameter.

9. A method of detecting respiratory disorders in an individual, the method comprising

sensing

a) mechanical movements of the individual'"'"'s chest;

and/or

b) acoustic waves generated by the individual'"'"'s heart beats;

processing said mechanical movements and/or acoustic waves to obtain one or more respiration-dependent parameters for subsequent computational analysis of the individual'"'"'s respiration pattern, respiration rate, other cardio-respiratory parameters or variations thereof, wherein one respiration-dependent parameter is a variation in S1-S2 intervals between two consecutive heart beats and another respiration-dependent parameter is a variation in heart sound amplitude.

10. The method of claim 9, wherein the computational analysis comprises

estimating S1-S2 interval variations between a beat and its preceding beat;

wherein

beats include both first (S1) and second (S2) heart sounds;

quantifying the similarity between the preceding beat and versions of the beat;

identifying maximum similarity throughout the versions and assigning a corresponding S1-S2 interval variation to the beat based on identified version.

11. The method of claim 9, wherein the heart beat is detected by its first heart sound S1, and wherein assessing a degree of similarity only includes the second sound S2.

12. The method of claim 9, wherein the heart beat is detected by its second heart sound S2, and wherein assessing a degree of similarity only includes the first sound S1.

13. The method of claim 9, wherein an additional respiration-dependent parameter is a variation in S1-S1 intervals.

14. The method of claim 9, wherein an additional respiration-dependent parameter is chest wall motion.

15. The method of claim 9, wherein the computational analysis is carried out with a combined plurality of respiration-dependent parameters.

16. The method of claim 9, wherein the computational analysis is carried out with one respiration-dependent parameter.

17. The method of claim 9, wherein the respiratory disorders are pulmonary hypertension, pulmonary edema, chronic obstructive pulmonary disease, asthma or sleep apnea.

18. A method of detecting an autonomic nervous system disorder in an individual, the method comprising

sensing

a) mechanical movements of the individual'"'"'s chest;

and/or

b) acoustic waves generated by the individual'"'"'s heart beats;

processing said mechanical movements and/or acoustic waves to obtain one or more respiration-dependent parameters for subsequent computational analysis of the individual'"'"'s respiration pattern, respiration rate, other cardio-respiratory parameters or variations thereof, wherein

one respiration-dependent parameter is a variation in S1-S2 intervals between two consecutive heart beats and another respiration-dependent parameter is a variation in heart sound amplitude.

19. The method of claim 18, wherein the computational analysis comprises

estimating S1-S2 interval variations between a beat and its preceding beat;

wherein

beats include both first (S1) and second (S2) heart sounds;

quantifying the similarity between the preceding beat and versions of the beat;

identifying maximum similarity throughout the versions and assigning a corresponding S1-S2 interval variation to the beat based on identified version.

20. The method of claim 18, wherein the heart beat is detected by its first heart sound S1, and wherein assessing a degree of similarity only includes the second sound S2.

21. The method of claim 18, wherein the heart beat is detected by its second heart sound S2, and wherein assessing a degree of similarity only includes the first sound S1.

22. The method of claim 18, wherein an additional respiration-dependent parameter is a variation in S1-S1 intervals.

23. The method of claim 18, wherein an additional respiration-dependent parameter is chest wall motion.

24. The method of claim 18, wherein the computational analysis is carried out with a combined plurality of respiration-dependent parameters.

25. The method of claim 18, wherein the computational analysis is carried out with one respiration-dependent parameter.

26. The method of claim 18, wherein the autonomic nervous system disorder is syncope.

27. A system of determining an individual'"'"'s respiration pattern, respiration rate, other cardio-respiratory parameters or variations thereof, the system comprising

at least one sensor for sensing

a) mechanical movements of the individual'"'"'s chest;

and/or

b) acoustic waves generated by the individual'"'"'s heart beats;

a data acquisition device for receiving signals derived from said mechanical movements and/or acoustic waves;

a processor for processing said signals to obtain one or more respiration-dependent parameters for subsequent computational analysis of the individual'"'"'s respiration pattern, respiration rate, other cardio-respiratory parameters or variations thereof.

28. The system of claim 27, wherein the computational analysis comprises

estimating S1-S2 interval variations between a beat and its preceding beat;

wherein

beats include both first (S1) and second (S2) heart sounds;

quantifying the similarity between the preceding beat and versions of the beat;

identifying maximum similarity throughout the versions and assigning a corresponding S1-S2 interval variation to the beat based on identified version.

29. The method of claim 27, wherein the heart beat is detected by its first heart sound S1, and wherein assessing a degree of similarity only includes the second sound S2.

30. The method of claim 27, wherein the heart beat is detected by its second heart sound S2, and wherein assessing a degree of similarity only includes the first sound S1.

31. The system of claim 27, wherein an additional respiration-dependent parameter is a variation in S1-S1 intervals.

32. The system of claim 27, wherein an additional respiration-dependent parameter is chest wall motion.

33. The system of claim 27, wherein the computational analysis is carried out with a combined plurality of respiration-dependent parameters.

34. The system of claim 27, wherein the computational analysis is carried out with one respiration-dependent parameter.

35. The system of claim 27, wherein the at least one sensor consists of a single-axis accelerometer, a multi-axis accelerometer, a stethoscope, a laser vibrometer or an electromagnetic radar.

36. The system of claim 27, wherein at least one sensor consists of a multi-axis accelerometer, and provides body posture and body motion information.

37. The system of claim 36 for the particular use as a sleep monitoring device.