Apparatus and method for detection of one lung intubation by monitoring sounds

• US 20090024046A1
• Filed: 04/03/2005
• Published: 01/22/2009
• Est. Priority Date: 04/04/2004
• Status: Abandoned Application

• Current Assignee: Ben Gurion University of The Negev, Mor Research Applications Ltd. (Clalit Health Services)
• Original Assignee: Ben Gurion University of The Negev, Mor Research Applications Ltd. (Clalit Health Services)
• Inventors: Cohen, Boaz, Weizman, Lior, Gurman, Gabriel, Cohen, Arnon, Tejman-Yarden, Noa, Tabrikian, Joseph
• Application Number:
  US10/599,598
• Publication Number:
  US 20090024046A1
• Time in Patent Office: Days
• US Class Current: 600/529
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise

Abstract

A method and a device for the acoustic detection of a one lung intubation situation in a human subject are disclosed. According to some embodiments, the disclosed method includes computing an autoregressive moving average (ARMA) or an autoregressive function of electrical signals received from acoustic detectors placed at different locations on the body of the subject. Appropriate locations for acoustic detectors include the back region and the chest region. The disclosed method and apparatus are insensitive to uncancelled, random background noise with a loudness associated with an operating room or intensive care ward. The disclosed device is configurable so that the relative occurrence rate of missed detections or false negatives and false positive alarms can be modified. In one exemplary embodiment, the device is adapted such that at most 9% of identifications are false positive identifications, and at most 2% of identifications are false negative identifications.

Claims

1-33. -33. (canceled)

34. ) A method of detecting a one lung ventilation situation in a human subject, the method comprising:

a) electronically detecting indigenous lung sounds emanating from a region of the body with an acoustic sensors; and

b) generating an output indicative of the one lung ventilation situation by processing said detected indigenous lung sounds.

35. ) The method of claim 34 wherein said detecting includes receiving a plurality of electrical signals from a plurality of acoustic sensors, wherein at least one said acoustic sensor is disposed adjacent to a chest region of the body.

36. ) The method of claim 34 wherein said detecting includes receiving a plurality of electrical signals from a plurality of acoustic sensors, wherein at least one said acoustic sensor is disposed adjacent to a back region of the body.

37. ) The method of claim 34 wherein said detecting includes receiving a plurality of electrical signals from a plurality of acoustic sensors, and said acoustic sensors are disposed only adjacent to regions of the body selected from the group consisting of a chest region of the body and a back region of the body.

38. ) The method of claim 34 wherein said detecting includes receiving a plurality of electrical signals from a plurality of acoustic sensors, and said processing includes computing a parameter indicative of a relation between a said electrical signal and at least one of a history of said electrical signal and a future behavior of said electrical signal.

39. ) The method of claim 36 wherein said processing includes computing a parameter indicative of a relation between a said electrical signal and at least one of a history of said electrical signal and a future behavior of said electrical signal.

40. ) The method of claim 38 wherein said processing includes computing a parameter indicative of a relation between a said electrical signal during a first time window and a said electrical signal during a second time window, wherein said first and second time windows overlap.

41. ) The method of claim 40 wherein said first and second time windows overlap by at least 1 second.

42. ) The method of claim 41 wherein said first and second time windows overlap by at least 2 seconds.

43. ) The method of claim 34 wherein said detecting includes receiving a plurality of electrical signals from a plurality of acoustic sensors, and said processing includes computing a parameter indicative of a relation between a first said electrical signal received from a first said acoustic sensor and at least one of a history of said first electrical signal received from said first acoustic sensor and a future behavior of said first electrical signal received from said first acoustic sensor.

44. ) The method of claim 43 wherein said first acoustic sensor is disposed adjacent to a chest region of the body.

45. ) The method of claim 43 wherein said first acoustic sensor is disposed adjacent to a back region of the body.

46. ) The method of claim 38 wherein said relation is indicative of a conditional probability relation.

47. ) The method of claim 46 wherein said processing includes computing a parameter related to a covariance matrix of said conditional probability relation.

48. ) The method of claim 46 wherein said processing includes computing at least one eigenvalue of said covariance matrix.

49. ) The method of claim 48 wherein a lower magnitude of one said eigenvalue is indicative of the one lung ventilation situation.

50. ) The method of claim 34 wherein said processing includes obtaining a parameter indicative of at least one type of statistics selected from the group consisting of spatial statistics and temporal statistics of said indigenous lung sounds.

51. ) The method of claim 50 wherein a deviation in said statistics is indicative of a change in an intubation status.

52. ) The method of claim 34 wherein said processing includes determining a number of distributed random sources of said indigenous lung sounds.

53. ) The method of claim 52 wherein said processing includes determining only a number of distributed random sources of said indigenous lung sounds.

54. ) The method of claim 34 wherein said processing includes determining a source scattering parameter indicative of a scattering of noise sources.

55. ) The method of claim 34 wherein said detecting includes generating an electrical signal, and said processing includes computing an estimate of an autoregressive moving average (ARMA) function of said electronic signal.

56. ) The method of claim 34 wherein said detecting includes generating an electrical signal, and said processing includes computing an estimate an autoregressive function of said electronic signal.

57. ) A method of detecting a one lung ventilation situation in a human subject, the method comprising:

a) electronically detecting noise including indigenous lung sounds emanating from a region of the body; and

b) generating an output indicative of the one lung ventilation situation by processing said detected indigenous lung sounds in way that is insensitive to uncancelled, random background noise of a loudness associated with an operating room.

58. ) The method of claim 57 wherein said uncancelled random background noise includes at least 70 decibels of noise.

59. ) The method of claim 57 wherein said stage of detecting include detecting noise other than lung sounds, and said stage of processing includes using an adaptive filtering technique to filter noise.

60. ) A method comprising:

a) selecting a population of human subjects sufficiently large to give statistically significant results; and

b) identifying a one lung intubation situation in a subpopulation of said population,

wherein at most 9.6% of said identifications are misidentifications.

61. ) A method comprising:

a) selecting a population of human subjects sufficiently large to give statistically significant results; and

b) identifying a one lung intubation situation in a subpopulation of said population,

wherein at most 4.8% of said identifications are false positive identifications, and at most 4.8% of said identifications are false negative identifications.

62. ) A method comprising:

a) selecting a population of human subjects sufficiently large to give statistically significant results; and

b) identifying a one lung intubation situation in a subpopulation of said population,

wherein at most 9% of said identifications are false positive identifications, and at most 2% of said identifications are false negative identifications.

63. ) A device for detecting a one lung ventilation situation in a subject, the device comprising:

a) a plurality of acoustic sensors adapted to electronically detect indigenous lung sounds emanating from a region of the body; and

b) a processing unit adapted to process an electronic signal received from said acoustic sensors and to generate an output indicative of a one lung ventilation situation.

64. ) The device of claim 63 wherein said processing unit is adapted such that at most 9% of said identification are false positive identifications, and at most 2% of said identifications are false negative identifications.

65. ) The device of claim 63 wherein said processing unit is adapted such that at most 4.5% of said identification are false positive identifications, and at most 4.5% of said identifications are false negative identifications.

66. ) The device of claim 63 wherein said processing unit is adapted to said output in way that is insensitive to uncancelled, random background noise of a loudness associated with an operating room.