Apparatus, system, and method for monitoring physiological signs

• US 8,562,526 B2
• Filed: 06/01/2007
• Issued: 10/22/2013
• Est. Priority Date: 06/01/2006
• Status: Active Grant

• Current Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Original Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Inventors: Heneghan, Conor, Hanley, Conor, Fox, Niall, De Chazal, Philip
• Primary Examiner(s): Thomson, Bill
• Assistant Examiner(s): Sands, Davin K
• Application Number:
  US12/302,704
• Publication Number:
  US 20090203972A1
• Time in Patent Office: 2,335 Days
• Field of Search: None
• US Class Current: 600/301
• 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 5/0507 using microwaves or terahertz waves
• 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 5/4818 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: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

An apparatus, system, and method monitors the motion, breathing, heart rate and sleep state of subjects, e.g., humans, in a convenient, non-invasive/non-contact, and low-cost fashion. More particularly, the motion, breathing, and heart rate signals are obtained through processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Periods of sleep disturbed respiration, or central apnea can be detected through analysis of the respiratory signal. The mean heart rate, and derived information, such as the presence of cardiac arrhythmias can be determined from the cardiac signal. Motion estimates can be used to recognize disturbed sleep and periodic limb movements. The sleep state may be determined by applying a classifier model to the resulting streams of respiratory, cardiac and motion data. A means for display of the sleep state, respiratory, cardiac, and movement status may also be provided.

Claims

1. An apparatus useful in detecting and analyzing one or more of respiration, cardiac activity, and bodily function or movement of a living subject, the apparatus comprising:

a multiplier circuit arranged to multiply a reflected signal with a transmitted pulsed signal and to output a baseband signal representing one or more of respiration, cardiac, and bodily function or movement;

a processor configured to analyze the baseband signal, determine a phase difference between the signal reflected from the subject and the pulsed signal transmitted towards the subject, and derive measurements of said one or more of respiration, cardiac activity, and bodily function or movement from the phase difference; and

wherein the processor is configured to run a classifier model to determine sleep disorder parameters by combining one or more of bodily function or movement, respiration, and cardiac activity.

2. The apparatus of claim 1, further comprising a sensor coupled to the processor and arranged to receive the signal reflected from the subject.

3. The apparatus of claim 1, wherein the reflected signal is associated with a transmitter collocated with the apparatus.

4. The apparatus of claim 3, wherein the transmitter is configured to generate an radio-frequency (RF) energy signal compatible for use with the living subject.

5. The apparatus of claim 1, wherein the processor is configured to recognize periods of bodily motion of the subject by identifying peaks in an energy envelope of the baseband signal.

6. The apparatus of claim 1, wherein the processor is configured to recognize periods of bodily motion of the subject by counting a number of threshold-crossings of the baseband signal per unit time.

7. The apparatus of claim 1, further comprising a sensor and wherein the processor is configured to receive the baseband signal and to output a processed signal in response to a signal received from the sensor, wherein the processor is further configured to use the processed signal to distinguish breathing activity of the subject.

8. The apparatus of claim 1, further comprising a sensor and wherein the processor is configured to receive an output of the sensor and to output a processed signal in response thereto, wherein the processor is further configured to use the processed signal to distinguish a cardiac activity of the subject.

9. The apparatus of claim 1, wherein the processor is configured to determine an activity count for a measurement epoch by calculating an energy of the baseband signal relative to other epochs.

10. The apparatus of claim 1, wherein the classifier model determines a Cheyne-Stokes respiration pattern by processing features obtained from a respiratory signal derived from the baseband signal.

11. The apparatus of claim 1, wherein the processor is configured to determine an Apnea Hypopnoea Index (AHI) by processing a respiratory signal derived from the baseband signal.

12. The apparatus of claim 11, wherein the AHI is determined solely by a derived respiratory effort parameter.

13. The apparatus of claim 1, wherein the processor is configured to determine a sleeping status of the subject by analysis of a motion signal derived from the baseband signal.

14. The apparatus of claim 1, wherein the classifier model determines a sleep state by combining one or more of motion signals, breathing signals, and cardiac signals.

15. The apparatus of claim 1, wherein the processor is configured to recognize a central apnea condition by determining that breathing and motion activity of the subject are below a predetermined threshold for a period of time.

16. The apparatus of claim 1, wherein the processor is configured to recognize a respiratory distress condition of the subject by comparing a derived respiratory frequency with an existing set of respiratory measurements.

17. The apparatus of claim 1, further comprising a visual indication device by which a sleep status of the subject determined by the processor is signaled to a local or remote user.

18. The apparatus of claim 1, further comprising a visual indication device by which a breathing parameter of the subject determined by the processor is signaled to a local or remote user.

19. The apparatus of claim 1, further comprising a visual indication device by which a bodily movement of the subject determined by the processor is signaled to a local or remote user.

20. The apparatus of claim 1, further comprising a visual indication device by which a heart rate of the subject determined by the processor is signaled to a local or remote user.

21. The apparatus of claim 1, wherein the signal reflected from the subject comprises a radio-frequency (RF) signal.

22. A system for measuring and analyzing one or more of a respiration parameter, cardiac activity, and bodily movement or function of a living subject, the system comprising:

a transmitter arrangement configured to propagate a pulsed radio frequency (RF) signal toward the subject;

a receiver arranged to receive an RF signal reflected from the subject;

a multiplier circuit arranged to multiply the pulsed RF signal and the reflected RF signal and to determine a phase difference between the propagated pulsed RF signal and the reflected RF signal;

a processor arranged to analyze the phase difference to produce measurements of one or more of a respiration parameter, cardiac activity, and a bodily movement or function, and

wherein the processor is configured to run a classifier model to determine sleep disorder parameters by combining the measurements of one or more of bodily movement or function, the respiration parameter, and cardiac activity.

23. The system of claim 22, further comprising at least one auxiliary sensor coupled to the processor.

24. The system of claim 23, wherein said at least one auxiliary sensor comprises one or more of an acoustic sensor, temperature sensor, humidity sensor, and a light sensor.

25. A method for measuring and analyzing one or more physiological parameters of a living subject, the method comprising:

sensing a signal reflected from the living subject;

multiplying the reflected signal with a transmitted pulsed signal to produce a baseband signal representing one or more of respiration, cardiac, and bodily function or movement;

processing and analyzing the reflected baseband signal by determining a phase difference between the transmitted pulsed signal and the signal reflected from the living subject;

deriving, using a processor, the one or more physiological parameters pertaining to the living subject using the phase difference, said one or more physiological parameters comprising one or more of a respiration parameter, cardiac activity, and bodily movement or function of the subject; and

wherein the processor is configured to run a classifier model to determine sleep disorder parameters by combining the measurements of one or more of the physiological parameters.

26. The method of claim 25, further comprising the step displaying the one or more derived physiological parameters.

27. The method of claim 25, further comprising sounding an audible alarm in response to a determination that one or more of said one or more physiological parameters is outside a normal limit.

28. The method of claim 25, wherein said physiological parameters comprise an Apnoea-Hypopnoea Index (AHI) obtained by analyzing a respiratory signal derived from the reflected signal.

29. The method of claim 25, further comprising the classifier model determining an activity count.

30. The method of claim 29, further comprising using the determined activity count to determine said one or more physiological parameters.

31. The method of claim 25, wherein said step of sensing comprises sensing a radio-frequency (RF) signal.

32. A non-transitory computer-readable medium containing computer instructions thereon which, when executed on a computer, carry out the functions of measuring and analyzing one or more physiological parameter of a living subject by:

multiplying a signal reflected from a living subject and a pulsed signal transmitted towards the living subject to produce a baseband signal representing one or more of respiration, cardiac, and bodily function or movement;

processing and analyzing the baseband signal by determining a phase difference between the reflected signal and the pulsed signal;

deriving said one or more physiological parameters pertaining to the living subject using the phase difference, said one or more physiological parameters comprising one or more of a respiration parameter, cardiac activity, and bodily movement or function of the subject; and

running a classifier model to determine sleep disorder parameters by combining the measurements of one or more of the physiological parameters.

33. An apparatus useful in detecting and analyzing one or more of respiration, cardiac activity, and bodily function or movement of a living subject, the apparatus comprising:

a multiplier circuit arranged to multiply a reflected signal with a transmitted pulse signal and to output a baseband signal representing one or more of respiration, cardiac, and bodily function or movement;

a processor configured to analyze the baseband signal and to derive measurements of said one or more of respiration, cardiac activity, and bodily function or movement therefrom, wherein said processor determines a phase difference between the transmitted pulsed signal and said signal reflected from the subject to derive one or more of said measurements; and

wherein the processor is configured to run a classifier model to determine sleep disorder parameters by combining one or more of bodily function or movement, respiration, and cardiac activity.