METHOD AND APPARATUS FOR MONITORING CARDIO-PULMONARY HEALTH

• US 20150164375A1
• Filed: 05/30/2013
• Published: 06/18/2015
• Est. Priority Date: 05/30/2012
• Status: Active Grant

• Current Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Original Assignee: Resmed Limited (ResMed Incorporated), ResMed Sensor Technologies Limited (ResMed Incorporated)
• Inventors: Heneghan, Conor, Schindhelm, Klaus Henry, Farrugia, Steven Paul, Colefax, Michael Waclaw, Javed, Faizan, Khushaba, Rami, De Chazal, Philip, Zaffaroni, Alberto, Fox, Niall, Celka, Patrick, O' Hare, Emer, Redmond, Stephen James
• Granted Patent: US 10,426,380 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0507 using microwaves or terahertz waves
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/097 Devices for facilitating collection of breath or for directing breath into or through measuring devices
• CPC Class Codes: A61B 5/103 Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes A61B5/08 takes precedence; measuring aids for tailors A41H1/00; measuring instruments specially adapted for dentistry A61C19/04
• CPC Class Codes: A61B 5/1115 Monitoring leaving of a patient support, e.g. a bed or a wheelchair
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1126 using a particular sensing technique
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• 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 5/7275 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 5/742 using visual displays A61B5/339 takes precedence
• 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: G08B 21/02 Alarms for ensuring the safety of persons
• CPC Class Codes: G16H 40/63 for local operation
• 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

Abstract

Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Claims

1. A cardio-pulmonary health monitoring apparatus comprising:

a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session;

a processor; and

a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals, the method comprising;

extracting one or more sleep disordered breathing features from the one or more movement signals, and

predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

2. A cardio-pulmonary health monitoring apparatus according to claim 1, wherein the contactless motion sensor, the processor, and the memory form part of a contactless sensor unit.

3. A cardio-pulmonary health monitoring apparatus according to claim 1, wherein the processor and the memory form part of a computing device that is external to the contactless motion sensor.

4. A cardio-pulmonary health monitoring apparatus according to claim 2, wherein the method further comprises sending an alert message to an external computing device dependent on the prediction.

5. A cardio-pulmonary health monitoring apparatus according to any of claims 1 to 4, further comprising a display, wherein the method further comprises displaying an alert message on the display dependent on the prediction.

6. A cardio-pulmonary health monitoring apparatus according to any of claims 1 to 5, further comprising an audio output, wherein the method further comprises generating an alarm by the audio output dependent on the prediction.

7. A cardio-pulmonary health monitoring apparatus according to any of claims 1 to 6, wherein the contactless motion sensor is a radio-frequency motion sensor.

8. A cardio-pulmonary health monitoring apparatus according to claim 7, wherein the radio-frequency motion sensor is configured to generate two movement signals based on respective transmitted radio-frequency signals that are in quadrature with each other.

9. A cardio-pulmonary health monitoring apparatus according to claim 8, wherein the method comprises combining the two movement signals into a combined movement signal before the extracting.

10. A cardio-pulmonary health monitoring apparatus according to claim 8, wherein the method comprises combining the sleep disordered breathing features extracted from each movement signal before the predicting.

11. A cardio-pulmonary health monitoring apparatus according to any of claims 1 to 10, wherein the processor is further configured to extract the features from one or more movement signals obtained from a contact motion sensor.

12. A cardio-pulmonary health monitoring apparatus according to any of claims 1 to 11, wherein the processor is further configured to extract the features from data relevant to the cardio-pulmonary health of the patient obtained from a non-motion sensor.

13. A cardio-pulmonary health monitoring apparatus comprising:

at least one sensor adapted to generate data related to the cardio-pulmonary health of a patient;

a display; and

a controller, coupled to the at least one sensor, the controller being configured to

analyse the sensor data during one or more monitoring sessions;

trigger generation on the display of at least one query based on the analysis, the query being configured to prompt the patient for a response; and

trigger generation of a clinical alert based on a patient response to the query that is input to the controller.

14. A cardio-pulmonary health monitoring apparatus according to claim 13, further comprising a memory including a data structure of questions, each question being associated with a detectable condition of the at least one respiratory parameter.

15. A cardio-pulmonary health monitoring apparatus according to claim 14, wherein a question addresses medication compliance.

16. A cardio-pulmonary health monitoring apparatus according to claim 14, wherein a question addresses dietary compliance.

17. A cardio-pulmonary health monitoring apparatus according to any of claims 14 to 16, wherein the controller is further configured to select a first question in response to a detected condition of the respiratory parameter, and based on a response to the first question, continue to analyse the at least one respiratory parameter in a subsequent monitoring session before generating a clinical alert.

18. A cardio-pulmonary health monitoring apparatus according to claim 17, wherein the controller is further configured to trigger generation of the clinical alert following the subsequent monitoring session, and the generation of the clinical alert is based on a patient response to a second question generated by the controller.

19. A cardio-pulmonary health monitoring apparatus according to any of claims 13 to 18, wherein the controller is further configured to adjust a treatment control parameter of a PAP device configured to generate a flow of breathable gas to a patient interface based on a patient response to the at least one query.

20. A cardio-pulmonary health monitoring apparatus according to any of claims 13 to 19, wherein triggering generation of the clinical alert comprises displaying a warning on a display.

21. A cardio-pulmonary health monitoring apparatus according to any of claims 13 to 20 further comprising a communications device, wherein triggering generation of the clinical alert comprises transmitting an electronic message from the communications device.

22. A cardio-pulmonary health monitoring apparatus according to any of claims 13 to 21, wherein the analysis comprises extracting features related to the cardio-pulmonary health of the patient from the data.

23. A cardio-pulmonary health monitoring apparatus according to claim 22, wherein the data are movement signals representing the movement of a patient, and the features are sleep disordered breathing features.

24. A cardio-pulmonary health monitoring apparatus according to claim 23, wherein the analysis further comprises predicting whether a clinical event is likely to occur within a predetermined prediction horizon based on the extracted features.

25. A cardio-pulmonary health monitoring apparatus comprising:

at least one sensor adapted to generate data related to the cardio-pulmonary health of a patient; and

a controller, coupled to the at least one sensor, the controller being configured to

measure at least one respiratory parameter from the data during one or more monitoring sessions;

analyse the at least one respiratory parameter; and

generate a potential relapse alert based on the prediction.

26. A cardio-pulmonary health monitoring apparatus according to claim 25, wherein the at least one respiratory parameter comprises a breathing rate.

27. A cardio-pulmonary health monitoring apparatus according to claim 25, wherein the at least one respiratory parameter comprises a measure of ventilation.

28. A cardio-pulmonary health monitoring apparatus according to any of claims 25 to 27, wherein the analysis of the at least one respiratory parameter comprises assessment of a change in the at least one respiratory parameter over the one or more monitoring sessions.

29. A cardio-pulmonary health monitoring apparatus according to any of claims 25 to 28, wherein the analysis of the at least one respiratory parameter comprises computing a probability of a relapse occurring.

30. A cardio-pulmonary health monitoring apparatus according to any of claims 25 to 28, wherein the analysis of the at least one respiratory parameter comprises computing a Boolean-valued potential relapse indicator.

31. A cardio-pulmonary health monitoring apparatus according to claim 30, wherein the computing comprises a comparison of changes in the one or more respiratory parameters with respective thresholds.

32. A cardio-pulmonary health monitoring apparatus according to any of claims 25 to 31, wherein the generating a potential relapse alert comprises displaying the potential relapse alert on a display.

33. A cardio-pulmonary health monitoring apparatus according to any of claims 25 to 32, wherein the generating a potential relapse alert comprises sending the potential relapse alert to a user of the apparatus.

34. A cardio-pulmonary health monitoring apparatus according to any of claims 25 to 33, wherein the data are movement signals representing the movement of a patient, and the respiratory parameters are sleep disordered breathing features.

35. A cardio-pulmonary health monitoring apparatus comprising:

a contactless sensor unit comprising a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient,

a processor, and

a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals, the method comprising

extracting one or more sleep disordered breathing features from the one or more movement signals over a monitoring session, the sleep disordered breathing features being indicative of the severity of sleep-disordered breathing by the patient during the monitoring session.

36. A cardio-pulmonary health monitoring apparatus according to claim 35, wherein the method further comprises sending an alert message to an external computing device dependent on the extracted sleep disordered breathing features.

37. A cardio-pulmonary health monitoring apparatus according to claim 35 or claim 36, wherein the method further comprises displaying an alert message on a display dependent on the extracted sleep disordered breathing features.

38. A cardio-pulmonary health monitoring apparatus according to any of claims 35 to 37, further comprising an audio output, wherein the method further comprises generating an alarm by the audio output dependent on the extracted sleep disordered breathing features.

39. A cardio-pulmonary health monitoring apparatus according to any of claims 35 to 38, wherein the one or more sleep disordered breathing features comprises a plurality of features from the group consisting of:

apnea/hypopnea index;

total sleep time;

ratio of the total duration of Cheyne-Stokes respiration events to the total sleep time; and

ratio of the total duration of Cheyne-Stokes respiration events to the duration of the monitoring session.

40. A method of monitoring cardio-pulmonary health of a patient, the method comprising:

extracting one or more sleep disordered breathing (SDB) features from one or more movement signals representing bodily movement of the patient, the movement signals being generated by a contactless motion sensor directed at the patient during at least one monitoring session, and

predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more SDB features.

41. A method according to claim 40, wherein the extracting comprises:

selecting sleep-and-breathing sections of the movement signals,

detecting SDB events in the sleep-and-breathing sections, and

calculating SDB features from the detected SDB events.

42. A method according to claim 41, wherein the extracting comprises first pre-processing the movement signals.

43. A method according to claim 42, wherein the number of movement signals is two, and the pre-processing comprises first combining the two movement signals.

44. A method according to claim 43, wherein the combining comprises:

removing the mean of each movement signal,

computing the minimum value of each movement signal,

computing the length of a sample vector whose components at each sample are the distances of the respective movement signals from their respective minimum values, and

removing the mean of the length at each sample.

45. A method according to any of claims 42 to 44, wherein the pre-processing comprises de-trending each movement signal.

46. A method according to any of claims 41 to 45, wherein the selecting further comprises selecting sleep-and-breathing sections of good quality.

47. A method according to claim 46, wherein sections of good quality comprise sections in which a figure of merit for the movement signal over the section exceeds a noise threshold for the section.

48. A method according to claim 47, wherein the figure of merit is the root mean square (RMS) value of the movement signal over the section.

49. A method according to claim 47, wherein the figure of merit is the 75^th percentile of the distribution of RMS values of the movement signal over the section.

50. A method according to any of claims 47 to 49, wherein the noise threshold for the section is the smaller of a maximum noise value for the motion sensor and a percentile of the distribution of the RMS values of the movement signal over the section.

51. A method according to any of claims 41 to 50, wherein sleep-and-breathing sections comprise sections during which the patient was asleep and not performing gross bodily movements.

52. A method according to claim 51, wherein sections in which the patient was asleep comprise epochs in which the patient is present and a waveform length value of the movement signal in the epoch is less than a threshold.

53. A method according to claim 51, wherein sections in which the patient was asleep comprise epochs labelled by an LDA classifier as asleep, wherein the LDA classifier is configured to label each epoch based on an activity count series and a movement flag series for the epoch.

54. A method according to claim 53, wherein the activity count series is obtained by integrating an envelope of the movement signal over one or more intervals within the epoch.

55. A method according to any of claims 51 to 54, wherein sections in which the patient was asleep and not performing gross bodily movements comprise sections in which the patient is asleep and a movement flag series indicating gross bodily movement is false.

56. A method according to any of claims 53 to 55, wherein the movement flag series is obtained by:

computing a noise feature, a correlation feature, and a power feature for each sample of the movement signal; and

setting a movement flag for each sample to true if a noise feature for the sample is above a high threshold and the corresponding correlation and power features are below a low threshold, otherwise setting the movement flag to false.

57. A method according to any of claims 51 to 56, wherein sections in which the patient was asleep and not performing gross bodily movements comprise epochs in which the patient is asleep and a variance of the movement signal within the epoch exceeds a threshold for the monitoring session.

58. A method according to claim 52, wherein epochs in which the patient is present are epochs in which a root mean square value of the movement signal within the epoch exceeds a noise-only threshold.

59. A method according to any of claims 41 to 58, wherein detecting SDB events comprises:

computing a correlation feature by correlating a respiratory effort envelope of each selected section of the movement signal with one or more respiratory effort templates, and

detecting an SDB event whenever the correlation feature exceeds a first threshold for a duration that is greater than a second threshold.

60. A method according to claim 59, wherein the correlation feature is the maximum of the correlation values over the respiratory effort templates.

61. A method according to claim 59 or claim 60, wherein the respiratory effort templates are respective generic templates whose duration has been scaled by a mean breathing modulation cycle length estimated for the patient over the monitoring session.

62. A method according to claim 61, wherein the breathing modulation cycle length is estimated as a weighted average of the reciprocal of the peak frequencies of the power spectral density of the respiratory effort envelope computed over one or more macro-epochs within the selected section.

63. A method according to claim 62, wherein the weighting is dependent on the heights of the respective peaks.

64. A method according to any of claims 41 to 63, wherein detecting SDB events comprises

computing a mean value of a respiratory effort envelope of each selected section over a main window,

calculating a baseline as the mean respiratory effort envelope value over two windows surrounding the main window, and

detecting an SDB event if the mean respiratory effort envelope value over a main window satisfies an apnea criterion or a hypopnea criterion with respect to the calculated baseline.

65. A method according to any of claims 41 to 64, further comprising confirming each detected SDB event as a valid SDB event.

66. A method according to claim 65, wherein the confirming comprises verifying that hyperpnea sections adjacent the minimum of a respiratory effort envelope corresponding to each detected event have a duration greater than a minimum value.

67. A method according to claim 66, wherein the confirming further comprises verifying that the respiratory effort envelope dips below a hypopnea threshold for a time greater than one fifth of a modulation cycle length for the patient.

68. A method according to claim 65, wherein the confirming comprises:

computing one or more features from the movement signal corresponding to each candidate SDB event, and

applying a rule-based inference engine to the one or more computed features to determine whether to confirm each detected SDB event.

69. A method according to any of claim 59 and claim 64 and claim 66, wherein the respiratory effort envelope is calculated for the selected section by lowpass filtering the modulus of a complex-valued signal whose real part is the movement signal of the selected section and whose imaginary part is the Hilbert transform of the movement signal of the section.

70. A method according to any of claim 59 and claim 64 and claim 66, wherein the respiratory effort envelope is calculated for the selected section as the empirical mode decomposition of the Hilbert transform of the movement signal of the selected section.

71. A method according to any of claim 59 and claim 64 and claim 66, wherein the respiratory effort envelope is calculated for the selected section by applying a double max and hold filter to the movement signal of the selected section to compute a positive envelope and a negative envelope, and subtracting the negative envelope from the positive envelope.

72. A method according to any of claims 41 to 71, wherein the calculating comprises computing statistics of the detected SDB events for the monitoring session.

73. A method according to claim 72, wherein the calculating further comprises calculating the statistics of the detected SDB events for each of multiple sub-sessions of the monitoring session.

74. A method according to claim 73, wherein the sub-sessions are halves of a main sleep period of the monitoring session.

75. A method according to claim 73, wherein the sub-sessions are quartiles of the monitoring session.

76. A method according to any of claims 72 to 75, wherein the calculating comprises computing statistics of the detected SDB events for multiple monitoring sessions.

77. A method according to any of claims 72 to 76, wherein the calculating further comprises normalising the calculated SDB features to zero mean and unity standard deviation over a sliding window comprising multiple monitoring sessions preceding and including the monitoring session.

78. A method according to claim 77, wherein the sliding window comprises all monitoring sessions preceding and including the monitoring session.

79. A method according to any of claims 40 to 78, wherein the predicting comprises applying a classifier to classify the SDB features into a “

no-event-occurring”

class or an “

event-occurring”

class.

80. A method according to claim 79, wherein the classifier is a linear discriminant analysis classifier.

81. A method according to claim 79, wherein the classifier is applied to one or more principal components of the SDB features.

82. A method according to any of claims 40 to 78, wherein the predicting comprises applying a heuristic rule to one or more principal components of the SDB features.

83. A method according to any of claims 40 to 82, further comprising:

triggering generation on a display of at least one query based on the prediction, the query being configured to prompt the patient for a response; and

triggering generation of a clinical alert based on a patient response to the query.

84. A method according to any of claims 40 to 82, further comprising generating a potential relapse alert based on the prediction.

85. A method of monitoring cardio-pulmonary health of a patient, the method comprising:

analysing data related to the cardio-pulmonary health of the patient;

triggering generation of at least one query on a display based on the analysis, the query being configured to prompt the patient for a response; and

triggering generation of a clinical alert based on a patient response to the query.

86. A method of monitoring cardio-pulmonary health of a patient, the method comprising:

measuring at least one respiratory parameter from data related to the cardio-pulmonary health of a patient during one or more monitoring sessions;

analysing the at least one respiratory parameter; and

generating a potential relapse alert based on the analysis of the at least one respiratory parameter.

87. A method of monitoring cardio-pulmonary health of a patient, the method comprising:

extracting one or more sleep disordered breathing features from one or more movement signals representing bodily movement of the patient, the movement signals being generated by a contactless motion sensor directed at the patient during at least one monitoring session, the sleep disordered breathing features being indicative of the severity of sleep-disordered breathing by the patient during the monitoring session.

88. A computer-readable medium storing program instructions configured to cause the processor to carry out a method of monitoring cardio-pulmonary health of a patient, the method comprising:

extracting one or more sleep disordered breathing (SDB) features from one or more movement signals representing bodily movement of the patient, the movement signals being generated by a contactless motion sensor directed at the patient during at least one monitoring session, and

predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more SDB features.

89. A computer-readable medium storing program instructions configured to cause the processor to carry out a method of monitoring cardio-pulmonary health of a patient, the method comprising:

analysing data related to the cardio-pulmonary health of the patient;

triggering generation of at least one query on a display based on the analysis, the query being configured to prompt the patient for a response; and

triggering generation of a clinical alert based on a patient response to the query.

90. A computer-readable medium storing program instructions configured to cause the processor to carry out a method of monitoring cardio-pulmonary health of a patient, the method comprising:

measuring at least one respiratory parameter from data related to the cardio-pulmonary health of a patient during one or more monitoring sessions;

analysing the at least one respiratory parameter; and

generating a potential relapse alert based on the analysis of the at least one respiratory parameter.

91. A computer-readable medium storing program instructions configured to cause the processor to carry out a method of monitoring cardio-pulmonary health of a patient, the method comprising:

extracting one or more sleep disordered breathing features from one or more movement signals representing bodily movement of the patient, the movement signals being generated by a contactless motion sensor directed at the patient during at least one monitoring session, the sleep disordered breathing features being indicative of the severity of sleep-disordered breathing by the patient during the monitoring session.