Airway instability detection system and method

• US 20060189880A1
• Filed: 11/16/2005
• Published: 08/24/2006
• Est. Priority Date: 01/27/1997
• Status: Active Grant

• Current Assignee: Lawrence A. Lynn
• Original Assignee: Lawrence A. Lynn
• Inventors: Lynn, Eric N., Lynn, Lawrence A.
• Granted Patent: US 9,468,378 B2
• Time in Patent Office: Days
• US Class Current: 600/538
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• 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 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence

Abstract

A system and method for data processing is disclosed. One example of a processor-based method comprises accessing data representative of a time series of at least one component of a timed waveform corresponding to a pulse, and searching the data for an occurrence of a cluster of variations induced by at least one of an apnea cluster and a hypopnea cluster. An exemplary system comprises a memory device adapted to access data representative of a time series of at least one component of a timed waveform corresponding to a pulse, and a processor adapted to search the data for an occurrence of a cluster of variations induced by at least one of an apnea cluster and a hypopnea cluster.

Claims

1. A processor-based method for processing data, comprising:

accessing data representative of a time series of at least one component of at least one timed waveform corresponding to a plethesmographic pulse; and

searching the data for an occurrence of a cluster of variations in said waveform induced by at least one of an apnea cluster and a hypopnea cluster.

2. The method recited in claim 1, wherein the cluster of variations corresponds to variations of an amplitude of the plethesmographic pulse.

3. The method recited in claim 1, wherein the time series comprises at least another component of the timed waveform, the at least another component corresponding to a heart rate.

4. The method recited in claim 1, comprising identifying the cluster of variations.

5. The method recited in claim 4, comprising providing an indication responsive to identifying the cluster of variations.

6. The method recited in claim 1, wherein the time series is comprised of at least one component of the timed waveform.

7. The method recited in claim 1, wherein the time series is derived from at least one component of the timed waveform.

8. The method recited in claim 1, wherein the time series is induced by at least one component of the timed waveform.

9. The method recited in claim 1, wherein the cluster of variations is indicative of a severity level of sleep disordered breathing.

10. The method recited in claim 1, wherein the cluster of variations is indicative of a severity level of ventilation instability.

11. The method recited in claim 1, wherein the cluster of variations is indicative of at least one of a presence and a severity level of sleep apnea.

12. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a pressure.

13. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a nasal pressure.

14. The method recited in claim 1, wherein the at least one component of a timed waveform comprises an impedance.

15. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a chest impedance.

16. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a chest movement.

17. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a systolic time interval.

18. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a QRS morphology.

19. The method recited in claim 1, wherein the at least one component of a timed waveform comprises an abdominal movement.

20. The method recited in claim 1, wherein the at least one component of a timed waveform comprises an airflow.

21. The method recited in claim 1, wherein the at least one component of a timed waveform comprises minute ventilation.

22. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a ventilation of at least one gas.

23. The method recited in claim 1, wherein the at least one component of a timed waveform comprises tidal carbon dioxide.

24. The method recited in claim 1, wherein the at least one component of a timed waveform comprises end tidal carbon dioxide.

25. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a blood pressure.

26. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a heart rate.

27. The method recited in claim 1, wherein the at least one component of a timed waveform comprises an oral pressure.

28. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a measurement made from an extrinsically applied positive pressure device.

29. The method recited in claim 28, wherein the measurement made from an extrinsically applied positive pressure device comprises a pressure.

30. The method recited in claim 28, wherein the measurement made from an extrinsically applied positive pressure device comprises a flow.

31. The method recited in claim 1, wherein the at least one component of a timed waveform comprises a transcutaneous measurement.

32. The method recited in claim 31, wherein the transcutaneous measurement relates to carbon dioxide.

33. The method recited in claim 1, wherein the at least one component of a timed waveform comprises an arousal.

34. The method recited in claim 33, wherein the arousal comprises an EEG arousal.

35. The method recited in claim 1, wherein the at least one component of a timed waveform is derived from a signal produced using a plethesmograph.

36. The method recited in claim 1, wherein the at least one component of a timed waveform is derived from a signal produced using a photoplethesmograph.

37. The method recited in claim 1, wherein the at least one component of a timed waveform comprises ventilation.

38. The method recited in claim 1, wherein the cluster of variations corresponds to variations of a shape of the plethesmographic pulse.

39. The method recited in claim 1, wherein the cluster of variations corresponds to variations in a timing of the plethesmographic pulse.

40. The method recited in claim 1, wherein the cluster of variations corresponds to variations in at least one shape characteristic of the plethesmographic pulse.

41. The method recited in claim 1, wherein the cluster of variations corresponds to variations in at least one dimension of the plethesmographic pulse.

42. The method recited in claim 1, wherein the cluster of variations corresponds to variations in a dimensional value of the plethesmographic pulse.

43. The method recited in claim 1, wherein the cluster of variations corresponds to variations in at least one mathematical derivative.

44. The method recited in claim 1, wherein the cluster of variations corresponds to variations in a slope.

45. The method recited in claim 1, wherein the cluster of variations corresponds to variations in at least one derivative of a rise.

46. The method recited in claim 1, wherein the cluster of variations corresponds to variations in an area under a portion of the timed waveform.

47. The method recited in claim 1, wherein the cluster of variations corresponds to variations of at least one positional characteristic.

48. The method recited in claim 1, wherein the time series comprises at least another component of the timed waveform, the at least another component corresponding to an oxygen saturation derived of pulse oximetry.

49. The method recited in claim 1, wherein the cluster of variations comprises clusters of similar measurement values between at least two points.

50. The method recited in claim 1, wherein the cluster of variations corresponds to variations of similar distances between at least two points on the timed waveform.

51. The method recited in claim 1, comprising analyzing a cluster to determine a severity associated with the induced variations.

52. A system for processing data, comprising:

a memory device adapted to access data representative of a time series of at least one component of a timed waveform corresponding to a plethesmographic pulse; and

a processor adapted to search the data for an occurrence of a cluster of variations induced by at least one of an apnea cluster and a hypopnea cluster.

53. The system recited in claim 52, wherein the cluster of variations corresponds to a cluster of variations of an amplitude of the plethesmographic pulse.

54. The system recited in claim 52, wherein the time series comprises at least another component of the timed waveform, the at least another component corresponding to a heart rate.

55. The system recited in claim 52, wherein the processor is adapted to identify the cluster of variations.

56. The system recited in claim 55, wherein the processor is adapted to provide an indication responsive to identifying the cluster of variations.

57. A system for processing data, comprising:

means for accessing data representative of a time series of at least one component of a timed waveform corresponding to a plethesmographic pulse; and

means for searching the data for an occurrence of a cluster of variations induced by at least one of an apnea cluster and a hypopnea cluster.

58. A tangible machine-readable medium comprising:

code adapted to access stored data representative of a time series of at least one component of a parameter corresponding to a plethesmographic pulse; and

code adapted to search the data for an occurrence of a cluster of variations induced by at least one of an apnea cluster and a hypopnea cluster.

59. The tangible machine-readable medium recited in claim 58, comprising code adapted to evaluate the cluster of variations to identify a pattern that corresponds to a known condition.

60. The tangible machine-readable medium recited in claim 59, comprising code adapted to objectify the time series to identify a pattern that corresponds to a known condition.