Apparatus, system and method for chronic disease monitoring

• US 9,526,429 B2
• Filed: 02/06/2009
• Issued: 12/27/2016
• 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: Zaffaroni, Alberto, Shouldice, Redmond, Heneghan, Conor, De Chazal, Philip
• Primary Examiner(s): Thomson, William
• Assistant Examiner(s): SANDS, DAVIN K
• Application Number:
  US12/367,255
• Publication Number:
  US 20100204550A1
• Time in Patent Office: 2,881 Days
• Field of Search: None
• US Class Current: 1/1
• 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 hypoponeas, 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 non-contact biomotion sensor configured to;

transmit radio waves,

detect transmitted radio waves reflected by the subject'"'"'s body, the detected radio waves being modulated due to movement of the subject'"'"'s body, and

output a first respiratory output signal comprising a measured respiratory parameter of the subject, the respiratory parameter determined by analysis of the detected radio waves; and

an analyzer configured to receive the first respiratory output signal and a second input signal that provides subjective clinical symptom data from the subject, wherein the analyzer is further configured to;

determine a respiratory effort envelope of the first respiratory output signal;

derive parameters related to at least one of the respiratory effort envelope and the second input signal, the parameters including at least an Apnea-Hypopnea Index (AHI);

store the derived parameters in a database comprising previously derived parameters; and

provide an output that provides a health assessment of the subject based on the previously derived parameters and currently derived parameters, the health assessment indicating whether clinical deterioration has happened.

2. The system of claim 1, further comprising a cardiac sensor operatively coupled to the analyzer, wherein the database is further configured to store a plurality of cardiac features derived from a detected cardiac parameter in the database of previously derived parameters,

wherein the analyzer is configured to selectively combine a currently derived cardiac feature, the plurality of cardiac features, a currently derived respiratory feature, and a plurality of respiratory features to determine the health assessment of the subject.

3. The system of claim 1, further comprising a third input signal supplied to the analyzer that provides a measure of a body weight of the subject.

4. The system of claim 1, further comprising a non-respiratory input signal that is distinct from the first respiratory output signal and provides one or more physiological measurements selected from the group consisting of a blood pressure, a blood oxygen level, a measurement of B natriuretic peptides, and a measurement of C-reactive protein.

5. The system of claim 1, further comprising a third input signal supplied to the analyzer that provides a physiological measurement of a blood glucose level.

6. The system of claim 1, further comprising a data hub configured to exchange data at least between the sensor and the analyzer.

7. The system of claim 1, wherein a derived movement signal is calculated through a combination of two quadrature movement signals using a phase demodulation technique.

8. The system of claim 1, wherein the analyzer determines a respiratory pattern by analyzing the respiratory effort envelope.

9. The system of claim 1, wherein the analyzer is configured to determine an occurrence of apneas and hypopneas using the respiratory effort envelope.

10. The system of claim 1, wherein the analyzer is configured to determine an occurrence of periodic breathing using the respiratory effort envelope.

11. The system of claim 1, wherein the analyzer is configured to determine a characteristic time period of periodic breathing using the respiratory effort envelope.

12. The system of claim 1, wherein the first respiratory output signal comprises a respiration rate of the subject.

13. The system of claim 1, wherein the output comprises a proposed clinical intervention step at least based on the respiratory effort envelope.

14. The system of claim 1, wherein the analyzer is further configured to calculate a likelihood of a significant clinical deterioration having occurred based at least on the respiratory effort envelope.

15. The system of claim 1 further comprising a display operatively coupled to the analyzer such that a trend in the first respiratory output signal may be visualized.

16. The system of claim 1, wherein the sensor is configured to output a second output signal that comprises a composite signal that includes a a cardiac feature and a bodily motion feature, and

wherein the analyzer is configured to selectively combine the first respiratory output signal, the cardiac feature, and the bodily motion feature to determine the output that provides the health assessment of the subject.

22. The system of claim 1, wherein the output comprises a numerical value that provides a statistical prediction of the health assessment, and the health assessment is based on a comparison of the numerical value to a threshold.

23. The system of claim 1, wherein the analyzer is further configured to initiate a call or query for additional data in response to the health assessment;

and

wherein;

when more than two symptoms indicated from the subjective clinical symptom data, the output comprises initiation of a nurse call, and

when there is significant weight gain and periodic breathing, the output comprises initiation of a nurse call.

24. The system of claim 1, wherein the subjective clinical symptom data is only related to respiration and heart condition.

25. The system of claim 1, wherein the health assessment of the subject is based on parameters derived for subjects in a same demographic as the subject.

26. The system of claim 1, wherein additional parameters are derived from additional signals, the additional derived parameters a body weight of the subject upon waking and after going to the bathroom and an average nocturnal heart rate;

and

wherein the derived parameters and additional derived parameters are stored on a nightly basis.

17. A computer-implemented method for monitoring a subject, the method comprising:

using a non-contact biomotion sensor to transmit radio waves towards the subject;

using the sensor to detect transmitted radio waves reflected by the subject'"'"'s body, the detected radio waves being modulated due to movement of the subject'"'"'s body, and to measure a respiratory output signal by analyzing the detected radio waves;

determining, using a processor, a respiratory effort envelope of the respiratory output signal;

generating, with the processor, a plurality of respiratory features derived from the respiratory effort envelope;

receiving, at the processor, subjective clinical symptom data from the subject;

deriving, using the processor, parameters related to at least one of the plurality of respiratory features and the subjective clinical symptom data for a current time period, the parameters including at least an Apnea-Hypopnea Index (AHI) derived from respiratory features from the respiratory effort envelope;

storing, using the processor, the derived parameters in a database of previously derived parameters; and

providing, using the processor, an output that provides a health assessment of the subject based on the previously derived parameters and currently derived parameters, the health assessment indicating whether clinical deterioration has happened.

18. The method of claim 17, further comprising combining one or more of the plurality of respiratory features with a plurality of features based on cardiac measurements to derive the output.

19. The method of claim 17, further comprising combining one or more of the plurality of respiratory features with one or more physiological measurements to derive the output,

wherein the physiological parameters are selected from the group consisting of blood pressure, a blood oxygen level, a measurement of B natriuretic peptides, and a measurement of C-reactive protein.

20. The method of claim 17, further comprising combining one or more of the plurality of respiratory features with one or more physiological measurements selected from the group consisting of body weight and a blood glucose level.

21. A system for monitoring a subject, comprising:

a non-contact biomotion sensor configured to detect radio waves reflected by the subject'"'"'s body, the detected radio waves being modulated due to movement of the subject'"'"'s body, and to output a bodily movement signal over the course of a sleeping period,

an input to capture subjective clinical symptom data from the subject;

an analyzer configured to determine a respiratory movement signal through filtering of the bodily movement signal and to determine an Apnea-Hypopnea Index (AHI) based on a respiratory effort envelope of the respiratory movement signal, and

a database configured to store at least a plurality of respiratory features derived from the respiratory movement signal, including the AHI,

wherein the analyzer is configured to implement an automated classifier which combines the subjective clinical symptom data and the plurality of respiratory features from a current sleeping period, together with measurements from previous sleeping periods, to determine an output that provides a health assessment of the subject, the output comprising a numerical value that is compared to a threshold in providing the health assessment, the health assessment indicating whether clinical deterioration has happened.