SYSTEM AND METHOD FOR AUTOMATIC DETECTION OF A PLURALITY OF SP02 TIME SERIES PATTERN TYPES

• US 20110208539A1
• Filed: 05/06/2011
• Published: 08/25/2011
• Est. Priority Date: 05/17/2001
• Status: Active Grant

• Current Assignee: Lawrence A. Lynn
• Original Assignee: Lawrence A. Lynn
• Inventors: Lynn, Lawrence A.
• Granted Patent: US 8,862,196 B2
• Time in Patent Office: Days
• US Class Current: 705/2
• 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 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/0871 Peak expiratory flowmeters
• 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 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7267 involving training the classification device
• 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 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: A61M 16/0051 with alarm devices
• CPC Class Codes: A61M 16/026 specially adapted for predicting, e.g. for determining an information representative of a flow limitation during a ventilation cycle by using a root square technique or a regression analysis
• CPC Class Codes: A61M 16/085 Gas sampling
• CPC Class Codes: A61M 16/0858 Pressure sampling ports
• CPC Class Codes: A61M 2016/0027 pressure meter
• CPC Class Codes: A61M 2016/0042 : in the expiratory circuit
• CPC Class Codes: A61M 2205/50 : with microprocessors or computers
• CPC Class Codes: A61M 2205/502 : User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 2205/8206 : battery-operated
• CPC Class Codes: A61M 2230/205 : partial oxygen pressure (P-O2)
• CPC Class Codes: A61M 2230/432 : partial CO2 pressure (P-CO2)
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The disclosed embodiments relate to a patient monitor for evaluating a patient. The patient monitor may comprise a sensor that is adapted to detect a sound associated with breathing of the patient and to produce a first output indicative of the sound, an oximeter that is adapted to receive an input from the patient and to produce a second output indicative of oxygen saturation of blood of the patient, and a processor adapted to receive at least the first output and the second output and to correlate the first output with the second output.

Claims

1-26. -26. (canceled)

27. A method for processing data, comprising:

accessing data representative of a time series of at least one component of a parameter;

detecting a first variation in the data, the first variation having a first beginning value, a first end value and a first amplitude along the time series;

detecting a second variation in the data, the second variation having a second beginning value, a second end value and a second amplitude along the time series;

comparing at least one of the first beginning value, the first end value and the first amplitude to at least one of the second beginning value, the second end value and the second amplitude; and

detecting an incomplete reciprocation responsive to the comparing of the at least one of the first beginning value, the first end value and the first amplitude to the at least one of the second beginning value, the second end value and the second amplitude.

28. The method recited in claim 27, comprising identifying a predetermined condition based on a result of the act of comparing.

29. The method recited in claim 27, wherein at least the first variation comprises a defection event.

30. The method recited in claim 27, wherein at least the first variation comprises a trend event.

31. The method recited in claim 27, wherein at least the first variation is substantially unipolar.

32. The method recited in claim 27, wherein at least the first variation comprises a rise.

33. The method recited in claim 27, wherein at least the first variation comprises a fall.

34. The method recited in claim 27, wherein the first variation comprises a rise and the second variation comprises a fall.

35. The method recited in claim 34, comprising determining whether the first amplitude is less than the second amplitude.

36. The method recited in claim 34, comprising determining whether a peak value of the first variation is less than the second beginning value.

37. The method recited in claim 27, wherein the parameter comprises arterial oxygen saturation.

38. The method recited in claim 37, comprising deriving the arterial oxygen saturation data from a pulse oximeter.

39. The method recited in claim 27, wherein the parameter comprises carbon dioxide.

40. The method recited in claim 27, wherein the parameter comprises minute ventilation.

41. A method for processing data, comprising:

accessing data representative of a plurality of time series, each of the plurality of time series corresponding to at least one component of a parameter;

detecting a first variation in a subset of the data that corresponds to a first one of the plurality of time series, the first variation having a first beginning value, a first end value and a first amplitude along the first one of the plurality of time series;

detecting a second variation in a subset of the data that corresponds to a second one of the plurality of time series, the second variation having a second beginning value, a second end value and a second amplitude along the second one of the plurality of time series;

comparing at least one of the first beginning value, the first end value and the first amplitude to at least one of the second beginning value, the second end value and the second amplitude; and

detecting an incomplete reciprocation responsive to the comparing of the at least one of the first beginning value, the first end value and the first amplitude to the at least one of the second beginning value, the second end value and the second amplitude.

42. The method recited in claim 41, comprising identifying a predetermined condition based on a result of the act of comparing.

43. The method recited in claim 41, wherein at least the first variation comprises a defection event.

44. The method recited in claim 41, wherein at least the first variation comprises a trend event.

45. The method recited in claim 41, wherein at least the first variation is substantially unipolar.

46. The method recited in claim 41, wherein at least the first variation comprises a rise.

47. The method recited in claim 41, wherein at least the first variation comprises a fall.

48. The method recited in claim 41, wherein the first variation comprises a rise and the second variation comprises a fall.

51. The method recited in claim 41, wherein the parameter represented by the first time series comprises arterial oxygen saturation.

52. The method recited in claim 51, comprising deriving the arterial oxygen saturation data from a pulse oximeter.

53. The method recited in claim 51, wherein the parameter represented by the second time series comprises minute ventilation.

54. The method recited in claim 41, wherein the parameter represented by the first time series comprises carbon dioxide.

49. The method recited in claim 49, comprising determining whether the first amplitude is less than the second amplitude.

50. The method recited in claim 49, comprising determining whether a peak value of the first variation is less than the second beginning value.

55. A data processing system, comprising:

a memory that is adapted to store data representative of a time series of at least one component of a parameter; and

a processor that is adapted to;

detect a first variation in the data, the first variation having a first beginning value, a first end value and a first amplitude along the time series;

detect a second variation in the data, the second variation having a second beginning value, a second end value and a second amplitude along the time series;

compare at least one of the first beginning value, the first end value and the first amplitude to at least one of the second beginning value, the second end value and the second amplitude; and

detect an incomplete reciprocation responsive to the comparing of the at least one of the first beginning value, the first end value and the first amplitude to the at least one of the second beginning value, the second end value and the second amplitude.

56. The data processing system recited in claim 55, wherein the processor is adapted to identify a predetermined condition based on a result of comparing the at least one of the first beginning value, the first end value and the first amplitude to at least one of the second beginning value, the second end value and the second amplitude.

57. The data processing system recited in claim 55, wherein the parameter comprises arterial oxygen saturation.

58. The data processing system recited in claim 57, wherein the arterial oxygen saturation is derived from a pulse oximeter.

59. The data processing system recited in claim 55, wherein the parameter comprises carbon dioxide.

60. The data processing system recited in claim 55, wherein the parameter comprises minute ventilation.

61. A data processing system, comprising:

means for storing data representative of a time series of at least one component of a parameter; and

means for detecting a first variation in the data, the first variation having a first beginning value, a first end value and a first amplitude along the time series, detecting a second variation in the data, the second variation having a second beginning value, a second end value and a second amplitude along the time series, comparing at least one of the first beginning value, the first end value and the first amplitude to at least one of the second beginning value, the second end value and the second amplitude, and detecting an incomplete reciprocation responsive to the comparing of the at least one of the first beginning value, the first end value and the first amplitude to the at least one of the second beginning value, the second end value and the second amplitude.

62. The data processing system recited in claim 61, wherein the parameter comprises arterial oxygen saturation.

63. The data processing system recited in claim 62, wherein the arterial oxygen saturation is derived from a pulse oximeter.

64. The data processing system recited in claim 61, wherein the parameter comprises carbon dioxide.

65. A tangible machine-readable medium comprising:

code adapted to access data representative of a time series of at least one component of a parameter;

code adapted to detect a first variation in the data, the first variation having a first beginning value, a first end value and a first amplitude along the time series;

code adapted to detect a second variation in the data, the second variation having a second beginning value, a second end value and a second amplitude along the time series;

code adapted to compare at least one of the first beginning value, the first end value and the first amplitude to at least one of the second beginning value, the second end value and the second amplitude; and

code adapted to detect an incomplete reciprocation responsive to the comparing of the at least one of the first beginning value, the first end value and the first amplitude to the at least one of the second beginning value, the second end value and the second amplitude.

66. A method for processing data, comprising:

accessing data representative of a time series of at least one component of a parameter; and

searching for a subset of the data that is representative of an incomplete reciprocation.

67. The method recited in claim 66, wherein the parameter comprises arterial oxygen saturation.

68. The method recited in claim 67, comprising deriving the arterial oxygen saturation data from a pulse oximeter.

69. The method recited in claim 66, wherein the parameter comprises carbon dioxide.

70. The method recited in claim 67, comprising detecting a magnitude ratio associated with the incomplete reciprocation.

71. The method recited in claim 70, comprising detecting a morphology associated with the incomplete reciprocation.

72. The method recited in claim 68, comprising analyzing the incomplete reciprocation to determine a severity of the incomplete reciprocation

73. The method recited in claim 68, comprising adjusting a narcotic dose based on the act of detecting.

74. The method recited in claim 68, comprising adjusting a narcotic dose based on the act of detecting.

75. The method recited in claim 74, comprising adjusting a narcotic infusion pump.

76. The method recited in claim 68, comprising adjusting a positive airway pressure device based on the act of detecting.

77. The method recited in claim 68, comprising adjusting a drug infusion pump based on the act of detecting.

78. The method recited in claim 68, comprising providing an alarm responsive to the act of detecting.

79. The method recited in claim 67, wherein the parameter comprises minute ventilation.

80. A method for processing data, comprising:

accessing data representative of a time series of at least one component of a parameter; and

defining a plurality of programmatic objects that each represent a segment of the time series, the time series comprising a plurality of reciprocations, detecting an object comprising a first component of a reciprocation, detecting an object comprising a second component of a reciprocation, comparing the first object to the second object to detect an incomplete reciprocation.