Non-contact physiologic motion sensors and methods for use

• US 8,454,528 B2
• Filed: 04/03/2009
• Issued: 06/04/2013
• Est. Priority Date: 04/03/2008
• Status: Active Grant

• Current Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Original Assignee: Kai Medical, Inc.
• Inventors: Yuen, Andrea, Droitcour, Amy, Madsen, Anders Host, Park, Byung Kwon, Hourani, Charles El, Shing, Tommy
• Primary Examiner(s): Natnithithadha, Navin
• Assistant Examiner(s): MESSERSMITH, ERIC J
• Application Number:
  US12/418,518
• Publication Number:
  US 20100130873A1
• Time in Patent Office: 1,523 Days
• Field of Search: 600/407, 600/534, 340/447, 340/539.1, 340/539.12, 340/539.15, 342/28, 342/114, 342/115
• US Class Current: 600/534
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• 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 5/1126 using a particular sensing technique
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• 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 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: G01S 13/534 based upon amplitude or phase shift resulting from movement of objects, with reference to the surrounding clutter echo signal, e.g. non coherent MTi, clutter referenced MTi, externally coherent MTi
• CPC Class Codes: G01S 13/583 using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets
• CPC Class Codes: G01S 13/88 Radar or analogous systems specially adapted for specific applications electromagnetic prospecting or detecting of objects, e.g. near-field detection, G01V3/00
• CPC Class Codes: G01S 7/2886 using I/Q processing
• CPC Class Codes: G01S 7/358 using I/Q processing
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A radar-based physiological motion sensor is disclosed. Doppler-shifted signals can be extracted from the signals received by the sensor. The Doppler-shifted signals can be digitized and processed subsequently to extract information related to the cardiopulmonary motion in one or more subjects. The information can include respiratory rates, heart rates, waveforms due to respiratory and cardiac activity, direction of arrival, abnormal or paradoxical breathing, etc. In various embodiments, the extracted information can be displayed on a display.

Claims

1. A method of sensing motion using a motion sensor, the method comprising:

generating electromagnetic radiation from a source of radiation, wherein the frequency of the electromagnetic radiation is in the radio frequency range;

transmitting the electromagnetic radiation towards a subject using one or more transmitters;

receiving a radiation scattered at least by the subject using one or more receivers;

extracting a Doppler shifted signal from the scattered radiation;

transforming the Doppler shifted signal to a digitized motion signal, said digitized motion signal comprising one or more frames, wherein the one or more frames comprise time sampled quadrature values of the digitized motion signal;

demodulating said one or more frames using a demodulation algorithm executed by a processor to isolate a signal corresponding to a physiological movement of the subject or a part of the subject;

analyzing the signal to obtain information corresponding to a non-cardiopulmonary motion or other signal interference;

processing the signal to obtain information corresponding to the physiological movement of the subject or a part of the subject, substantially separate from said non-cardiopulmonary motion or other signal interference; and

communicating the information to an output system that is configured to perform an output action,

wherein demodulating said one or more frames comprises;

computing in the processor a first set of covariance matrices of a first subset of frames selected from said one or more frames and a second set of covariance matrices of a second subset of frames selected from said one or more frames;

determining a first A-matrix, wherein the first A-matrix comprises a weighted sum of the first set of covariance matrices;

determining a first parameter vector corresponding to a first primary value of the first A matrix;

storing the first parameter vector in a memory device which is in communication with the processor

determining a second A-matrix, wherein the second A-matrix comprises a weighted sum of the second set of covariance matrices;

determining a second parameter vector corresponding to a second primary value of the second A-matrix;

calculating an inner product of the first parameter vector and the second parameter vector;

multiplying the second parameter vector by the sign of the inner product and

projecting the values of the second frame on the second parameter vector to obtain the demodulated signal.

2. The method of claim 1, wherein the output system comprises a display unit configured to display the information.

3. The method of claim 1, wherein the output system comprises an audible system that is configured to report information or alerts audibly based on the information.

4. The method of claim 1, wherein the output system comprises an external medical system that is configured to perform an action based on the information.

5. The method of claim 1, wherein the demodulating algorithm comprises a linear demodulation algorithm, an arc-based demodulation algorithm or a non-linear demodulation algorithm.

6. The method of claim 1, wherein the information is displayed at least alphanumerically, graphically and as a waveform.

7. The method of claim 1, wherein the subject is a human being or an animal and the physiological movement comprises at least one of a motion due to respiratory activity of the subject, motion due to a cardiopulmonary activity of the subject, motion due to a cardiac activity of the subject, motion due to a cardiovascular activity of the subject, and motion due to a physical activity of the subject.

8. The method of claim 1, wherein the demodulating algorithm comprises projecting the signal in a complex plane on a best-fit line, projecting the signal in a complex plane on a principal eigenvector, or aligning a signal arc to a best-fit circle and using the best-fit circle parameters to extract the angular information from the signal arc.

9. The method of claim 1, wherein the first primary value comprises the largest eigenvalue of the first A-matrix and the first primary vector comprises an eigenvector corresponding to said eigenvalue.

10. The method of claim 1, wherein the second primary value comprises the largest eigenvalue of the second A-matrix and the second primary vector comprises an eigenvector corresponding to said eigenvalue.

11. The method of claim 1, wherein the source of radiation comprises an oscillator.

12. The method of claim 1, wherein said one or more transmitters comprise one or more antennae.

13. The method of claim 1, wherein said one or more receivers comprise one or more antennae or arrays of antennae.

14. The method of claim 1, wherein said transmitting and receiving antennae are the same antennae.

15. The method of claim 1, wherein the receiver comprises a homodyne receiver.

16. The method of claim 1, wherein the receiver comprises a heterodyne receiver.

17. The method of claim 1, wherein the receiver comprises a low-IF receiver configured to transform the Doppler-shifted signal to a Doppler-shifted signal comprising frequencies in a low intermediate frequency range, which is digitized and digitally transformed to a digitized motion signal.

18. The method of claim 1, wherein the processor comprises at least one of a digital signal processor, a microprocessor and a computer.

19. The method of claim 18, further comprising a controller configured to control the processor.

20. The method of claim 1, wherein the output system comprises a display unit configured to display information regarding the physiological movement of a user at a remote location.

21. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the absence of non-cardiopulmonary motion is detected if the signal comprises a single stable source or the presence of non-cardiopulmonary signal if at least the signal is unstable or at least the signal has multiple sources.

22. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if the signal indicates an excursion larger than the subject'"'"'s maximum chest excursion from cardiopulmonary activity.

23. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if a best-fit vector related to linear demodulation changes significantly.

24. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if a RMS difference between a complex constellation of the signal and a best fit vector related to linear demodulation changes significantly.

25. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if an origin or radius of a best-fit circle related to arc-based demodulation changes significantly.

26. The method of claim 1, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm configured to detect the presence of non-cardiopulmonary motion if a RMS difference between a complex constellation of the signal and a best-fit circle related to arc-based demodulation changes significantly.

27. The method of claim 1, further comprising communicating information related to a signal quality of a cardiopulmonary motion signal, based on at least one of:

a presence of non-cardiopulmonary motion or other signal interference, an absence of non-cardiopulmonary motion or other signal interference, a degree of non-cardiopulmonary motion or other signal interference, an assessment of the signal-to-noise ratio, a detection of low signal power, or a detection of signal clipping or other signal interference, to an output system configured to output the information.

28. A method of sensing motion using a motion sensor, the method comprising:

generating electromagnetic radiation from a source of radiation wherein the frequency of the electromagnetic radiation is in the radio frequency range;

transmitting the electromagnetic radiation towards a subject using one or more transmitters;

receiving a radiation scattered at least b the subject using one or more receivers;

extracting a Doppler shifted signal from the scattered radiation;

transforming the Doppler shifted signal to a digitized motion signal, said digitized motion signal comprising one or more frames, wherein the one or more frames comprise time sampled quadrature values of the digitized motion signal,

demodulating said one or more frames using a demodulation algorithm executed by a processor to isolate a signal corresponding to a physiological movement of the subject or a part of the subject;

analyzing the signal to obtain information corresponding to a non-cardiopulmonary motion or other signal interference, wherein analyzing the signal comprises executing a non-cardiopulmonary motion detection algorithm by a processor to detect the presence or absence of non-cardiopulmonary motion or other signal interference from the digitized motion signal, wherein the non-cardiopulmonary motion detection algorithm comprises a first mode which detects a presence of non-cardiopulmonary motion or other signal interference and a second mode which detects a cessation of non-cardiopulmonary motion or other signal interference;

processing the signal to obtain information corresponding to the physiological movement of the subject or a part of the subject, substantially separate from said non-cardiopulmonary motion or other signal interference; and

communicating the information to an output system that is configured to perform an output action.

29. The method of claim 28, wherein the first mode comprises:

selecting a first subset of frames from said one or more frames and computing in the processor a first set of covariance matrices of the first subset of frames filtered by a low-pass filter;

determining a first A-matrix wherein the A-matrix comprises a weighted sum of the first set of covariance matrices;

determining a first parameter vector corresponding to a first primary value of the first A matrix; and

storing the first parameter vector in a memory device which is in communication with the processor.

30. The method of claim 29, further comprising

computing in the processor a second set of covariance matrices of a second subset of frames filtered by the low-pass filter;

determining a second A-matrix, wherein the A-matrix comprises a weighted sum value of the second set of covariance matrices;

determining a first and a second primary value of the second A-matrix;

determining a second parameter vector corresponding to the first primary value of the second A-matrix;

calculating an inner product of the first parameter vector and the second parameter vector;

calculating a ratio of the first primary value of the second A matrix to the second primary value of the second A matrix;

calculating a first energy corresponding to the average energy of a third subset of frames filtered by a high-pass filter and a second energy corresponding to the average energy of a fourth subset of frames filtered by a high-pass filter; and

calculating a ratio of the second energy to the first energy.

31. The method of claim 29, wherein the first primary value of the first A-matrix comprises the largest eigenvalue of the first A-matrix and the first primary vector comprises an eigenvector corresponding to said eigenvalue.

32. The method of claim 30, wherein the first primary value of the second A-matrix comprises the largest eigenvalue of the second A-matrix, the second primary value of the second A-matrix comprises the second largest eigenvalue of the second A-matrix and the second primary vector of the second A-matrix comprises an eigenvector corresponding to said first primary value of the second A-matrix.

33. The method of claim 28, wherein the method further comprises:

computing in the processor a first condition, said first condition being the inner product is less than a first threshold value or the ratio of the first primary value of the second A-matrix to the second primary value of the second A-matrix is less than a second threshold value or the ratio of the second energy to the first energy is greater than a third threshold value,

wherein the presence of non-cardiopulmonary motion or other signal interference is detected if the first condition is true and the ratio of the second energy to the first energy is greater than a fourth threshold value.

34. The method of claim 33, wherein the first threshold value is approximately between 0.6 and 1.

35. The method of claim 33, wherein the second threshold value is approximately between 4 and 12.

36. The method of claim 33, wherein the third threshold value is approximately between 4 and 20.

37. The method of claim 33, wherein the fourth threshold value is approximately between 0.1 and 0.8.

38. The method of claim 28, wherein the second mode comprises:

selecting in the processor each and every consecutive subset of frames within a fifth subset of frames;

computing in the processor covariance matrices for every subset of frames;

computing in the processor an A′

-matrix for each subset of frames, wherein the A′

-matrix is the average of the covariance matrices in the subset;

computing in the processor a ρ

- matrix, wherein each element of the ρ

-matrix corresponds to a first primary vector of the corresponding A′

-matrix;

computing the inner product of each pair of primary vectors in the ρ

- matrix and selecting a minimum absolute value of the inner products;

calculating an A matrix which is the sum of the covariance matrices in a sixth subset of frames;

determining a first and a second primary value of the A-matrix; and

calculating the ratio of the first primary value of the A matrix to the second primary value of the A matrix.

39. The method of claim 38, wherein the method further comprises:

computing in the processor a second condition, said second condition being the minimum absolute value of the inner products is greater than a first threshold value and the ratio of the first primary value to the second primary value is greater than a second threshold value,

wherein the cessation of non-cardiopulmonary motion or other signal interference is detected if the second condition is true.

40. The method of claim 39, wherein the fifth threshold value is approximately between 0.6 and 1.

41. The method of claim 39, wherein the sixth threshold value is approximately between 4 and 12.

42. The method of claim 38, wherein the first primary vector comprises an eigenvector corresponding to the largest eigenvalue of the corresponding A′

-matrix

43. The method of claim 38, wherein the first primary value comprises the largest eigenvalue of the A-matrix and the second primary value comprises the second largest eigenvalue of the A-matrix.

44. The method of claim 38, further comprising a retrospect step configured to determine a frame from said one or more frames when the non-cardiopulmonary motion substantially ceased.

45. The method of claim 44, wherein one or more frames preceding said frame are discarded.

46. The method of claim 29, wherein the weighted sum is an arithmetic mean.