Apparatus, system and method for chronic disease monitoring

• US 10,799,126 B2
• Filed: 11/04/2016
• Issued: 10/13/2020
• Est. Priority Date: 02/06/2009
• Status: Active Grant

• Current Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Original Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Inventors: Heneghan, Conor, Zaffaroni, Alberto, De Chazal, Philip, Shouldice, Redmond
• Primary Examiner(s): Kahelin, Michael W
• Assistant Examiner(s): Huh, Vynn V
• Application Number:
  US15/343,994
• Publication Number:
  US 20170231504A1
• Time in Patent Office: 1,439 Days
• 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 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• 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 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• 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 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: G01S 13/50 Systems of measurement based on relative movement of target
• 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

An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypopneas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

Claims

1. A system for monitoring a subject, the system comprising:

a sensor configured to receive signals reflected from the subject'"'"'s body;

and

an input device configured to receive user-generated responses from the subject,

the system being configured to;

generate, using one or more processors, respiratory movement data from at least one reflected signal received by the sensor;

derive, using the one or more processors, a respiratory effort envelope from the respiratory movement data;

derive, using the one or more processors, at least one respiratory parameter from the respiratory effort envelope, the at least one respiratory parameter comprising an apnea hypopnea index or a periodic breathing index;

implement, using the one or more processors, an automated classifier that combines the at least one respiratory parameter with at least one user-generated response received by the input device to determine a numerical value; and

provide, using the one or more processors, an output pertaining to a health assessment of the subject based on a comparison between the numerical value and a predetermined threshold.

2. The system of claim 1, wherein the one or more processors comprise one or more local processors further configured to pre-process the at least one reflected signal to generate the respiratory movement data.

3. The system of claim 2, wherein the one or more local processors are configured to implement the automated classifier.

4. The system of claim 1, wherein the one or more processors comprise one or more remote processors, wherein the system further comprises a transmitter configured to transmit the at least one respiratory parameter and the at least one user-generated response to the one or more remote processors, and wherein the one or more remote processors are configured to implement the automated classifier.

5. The system of claim 1, wherein the one or more processors comprise one or more remote processors, wherein the system further comprises a transmitter configured to transmit the at least one reflected signal and the at least one user-generated response to the one or more remote processors, and wherein the one or more remote processors are configured to generate the respiratory movement data, derive the respiratory effort envelope, derive the at least one respiratory parameter, and implement the automated classifier.

6. The system of claim 1, wherein the sensor is configured to transmit radio waves and receive transmitted radio waves reflected from the subject'"'"'s body, and wherein the at least one reflected signal received by the sensor is a radio wave reflected from the subject'"'"'s body.

7. The system of claim 1, further comprising:

a database that is configured to store the at least one respiratory parameter and the at least one user-generated response.

8. The system of claim 1, further comprising:

a display operatively coupled to the one or more processors such that a trend in the health assessment of the subject may be visualized.

9. The system of claim 1, wherein the automated classifier further combines one or more physiological parameters selected from the group consisting of:

blood pressure, a forced expiratory volume, a peak expiratory flow, a blood oxygen level, a blood glucose level, a measurement of B natriuretic peptides, a measurement of C-reactive protein, significant bodily movement, and body weight, with the at least one respiratory parameter and the at least one user-generated response to determine the numerical value.

10. The system of claim 1, wherein the respiratory movement data is generated from a combination of two or more reflected quadrature signals received by the sensor.

11. The system of claim 1, wherein the system is further configured to determine, using the one or more processors, a proposed clinical intervention by applying a set of automated rules to the health assessment of the subject.

12. The system of claim 1, wherein the system is further configured to calculate, using the one or more processors, a likelihood of a clinical deterioration having occurred based on the comparison between the numerical value and the predetermined threshold.

13. The system of claim 1, wherein the at least one reflected signal comprises two or more components corresponding to respiratory effort and at least one of bodily movement or heart rate.

14. The system of claim 13, wherein the system is further configured to derive, using one or more processors, cardiac, and bodily motion parameters from the at least one reflected signal, and wherein the automated classifier further combines the cardiac and bodily motion parameters with the at least one respiratory parameter and the at least one user-generated response to determine the numerical value.

15. The system of claim 14, wherein the bodily motion parameter corresponds to a significant bodily movement.

16. The system of claim 1, wherein the at least one user-generated response includes symptom data from the subject.

17. The system of claim 1, wherein the at least one respiratory parameter comprises an apnea hypopnea index.

18. The system of claim 17, wherein deriving the apnea hypopnea index comprises comparing an overall amplitude of the respiratory effort envelope to one or more thresholds.

19. The system of claim 1, wherein the at least one respiratory parameter comprises a periodic breathing index.

20. The system of claim 19, wherein the periodic breathing index is derived from a power spectral density of the respiratory effort envelope.

21. The system of claim 1, wherein system is further configured to derive, using one or more processors, at least one additional respiratory parameter from the at least one reflected signal, wherein the at least one additional respiratory parameter comprises at least one of a respiratory rate, a respiratory effort, or a respiratory frequency, and wherein the automated classifier further combines the at least one additional respiratory parameter with the at least one respiratory parameter and the at least one user-generated response to determine the numerical value.

22. The system of claim 1, wherein the system is further configured to derive, using one or more processors, at least one respiratory frequency parameter from the at least one reflected signal, wherein the at least one respiratory frequency parameter comprises a median respiratory frequency, a variance of respiratory frequency, a percentile distribution of respiratory frequency, or an autocorrelation of respiratory frequency, and wherein the automated classifier further combines the at least one respiratory frequency parameter with the at least one respiratory parameter and the at least one user-generated response to determine the numerical value.

23. The system of claim 1, wherein the at least one user-generated response comprises demographic information.

24. The system of claim 23, wherein the demographic information includes the subject'"'"'s age or sex.

25. The system of claim 1, wherein the automated classifier combines the at least one respiratory parameter with the at least one user-generated response, in part, by mapping the at least one user-generated response to at least one numerical quantity.

26. A method for monitoring a subject, the method comprising:

receiving, at a sensor, at least one signal reflected from the subject'"'"'s body;

generating, by one or more processors, respiratory movement data from the at least one reflected signal;

receiving, at a data aggregation device, the at least one measurement of physiological data indicative of at least one physiological parameter of the subject;

receiving, at an input device, at least one user-generated response from the subject;

deriving, by the one or more processors, a respiratory effort envelope from the respiratory movement data;

deriving, by one or more processors, at least one respiratory parameter from the respiratory effort envelope, the at least one respiratory parameter comprising an apnea hypopnea index or a periodic breathing index;

implementing, by the one or more processors, an automated classifier that combines the at least one respiratory parameter with the at least one user-generated response to determine a numerical value; and

providing, by the one or more processors, an output pertaining to a health assessment of the subject based on a comparison between the numerical value and a predetermined threshold.

27. The method of claim 26, wherein the one or more processors comprise one or more local processors, and wherein the method further comprises:

pre-processing, by the one or more local processors, the at least one reflected signal to generate the respiratory movement data.

28. The method of claim 27, wherein the one or more local processors are used to implement the automated classifier.

29. The method of claim 26, wherein the one or more processors comprise one or more remote processors, wherein the method further comprises:

transmitting the at least one respiratory parameter and the at least one user-generated response to the one or more remote processors, and wherein the one or more remote processors are used to implement the automated classifier.

30. The method of claim 26, wherein the one or more processors comprise one or more remote processors, wherein the method further comprises:

transmitting the at least one reflected signal and the at least one user-generated response to the one or more remote processors, and wherein the one or more remote processors are used to generate the respiratory movement data, derive the respiratory effort envelope, derive the at least one respiratory parameter, and implement the automated classifier.

31. The method of claim 26, further comprising:

transmitting, from the sensor, radio waves,

wherein the at least one reflected signal received by the sensor is a radio wave reflected from the subject'"'"'s body.

32. The method of claim 26, further comprising:

storing, in a database, the at least one respiratory parameter and the at least one user-generated response.

33. The method of claim 26, wherein the at least one user-generated response includes symptom data from the subject.

34. The method of claim 26, further comprising:

applying, by the one or more processors, a set of automated rules to the at least one respiratory parameter and the at least one user-generated response to output a proposed clinical intervention.

35. The method of claim 26, further comprising:

calculating, by the one or more processors, a likelihood of a clinical deterioration having occurred based on the comparison between the numerical value and the predetermined threshold.

36. The method of claim 26, wherein the output includes the health assessment and at least one of the at least one respiratory parameter or the at least one user-generated response.

37. The method of claim 26, further comprising:

deriving, by the one or more processors, at least one additional respiratory parameter from the at least one reflected signal, wherein the at least one additional respiratory parameter comprises at least one of a respiratory rate, a respiratory effort, or a respiratory frequency, and

wherein the automated classifier further combines the at least one additional respiratory parameter with the at least one respiratory parameter and the at least one user-generated response to determine the numerical value.

38. The method of claim 26, further comprising:

deriving, by the one or more processors, at least one respiratory frequency parameter from the at least one reflected signal, wherein the at least one respiratory frequency parameter comprises a median respiratory frequency, a variance of respiratory frequency, a percentile distribution of respiratory frequency, or an autocorrelation of respiratory frequency, and

wherein the automated classifier further combines the at least one respiratory frequency parameter with the at least one respiratory parameter and the at least one user-generated response to determine the numerical value.

39. The method of claim 26, wherein the at least one user-generated response comprises demographic information.

40. The method of claim 39, wherein the demographic information includes user age or sex.

41. The method of claim 26, wherein the at least one respiratory parameter comprises an apnea hypopnea index, and wherein deriving the apnea hypopnea index comprises comparing an overall amplitude of the respiratory effort envelope to one or more thresholds.

42. The method of claim 26, wherein the at least one respiratory parameter comprises a periodic breathing index, and wherein the periodic breathing index is derived from a power spectral density of the respiratory effort envelope.