Combining measurements from breathing rate sensors

• US 7,318,808 B2
• Filed: 12/13/2002
• Issued: 01/15/2008
• Est. Priority Date: 12/14/2001
• Status: Expired due to Fees

• Current Assignee: Oxford University Innovation Limited (University of Oxford)
• Original Assignee: Isis Innovation Limited
• Inventors: Mason, Catherine Laura, Tarassenko, Lionel
• Primary Examiner(s): NASSER, ROBERT L
• Application Number:
  US10/498,673
• Publication Number:
  US 20050027205A1
• Time in Patent Office: 1,859 Days
• Field of Search: 600/300, 600/481, 600/529
• US Class Current: 600/529
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00

Abstract

A method for combining breathing rate measurements from two or more independent measurement channels. Independent measurements of breathing rate, for instance by impedance pneumography and photoplethysmography, can be combined to derive an improved measurement eliminating artefacts on one channel. A model of the process generating the breathing rate, is constructed and is run independently for each channel to generate predictions of the breathing rate. The model may be a Kalman filter. The measured values are compared with the predicted values and the difference is used as an indication of the confidence in the measurement, the higher the difference the lower the confidence. The measurements from the two channels are combined using weights calculated from the respected differences.

Claims

1. A method of measuring breathing rate of a subject comprising the steps of:

predicting a value of each of two independent measurements of the breathing rate for two independent measurement channels, making two independent measurements of the breathing rate via said two independent measurement channels to produce two measured values of the breathing rate, calculating respective differences between the predicted values and the measured values for each of said two independent measurement channels, and combining the two measured values with weights determined by said differences.

2. A method according to claim 1 in which the steps of prediction, measurement, calculation and combination are repeated continuously, the predicted value for each of the two independent measurements being based on the preceding predicted value and the difference between the preceding predicted value and the preceding measurement.

3. A method according to claim 2 in which the predicted value for each of the two independent measurements is calculated by using a linear predictive model.

4. A method according to claim 2 in which the predicted value for each of the two independent measurements is calculated by using a non-linear predictive model.

5. A method according to claim 2 in which the predicted value for each of the two independent measurements is calculated by using an adaptive predictive model, and wherein the model adapts in dependence upon the amount of process noise in the measurements.

6. A method according to claim 1 in which in the step of combining the two measured values the weight of each value varies inversely with the square of the difference between the predicted value and the measurement.

7. A method according to claim 6 in which the two measured values are combined according to the formula:

$\mathrm{BR}={\mathrm{BR}}_1<br><br>\frac{{\mathrm{\sigma <br><br>}}_2^2}{{\mathrm{\sigma <br><br>}}_1^2+{\mathrm{\sigma <br><br>}}_2^2}+{\mathrm{BR}}_2<br><br>\frac{{\mathrm{\sigma <br><br>}}_1^2}{{\mathrm{\sigma <br><br>}}_1^2+{\mathrm{\sigma <br><br>}}_2^2}$

where BR₁ and BR₂ are the two measured values, and σ

₁ and σ

₂ are the differences between the two measured values and their respective predicted values.

8. A method according to claim 1 in which the predicted values for the respective measurements are based on respective models of the system.

9. A method according to claim 8 in which the models of the system include estimates for process noise and sensor noise.

10. A method according to claim 8 in which the respective models of the system are mutually independent.

11. A method according to claim 10 in which the respective models of the system include the same estimates for process noise and sensor noise.

12. A method according to claim 8, in which the respective models of the system are Kalman filters.

13. A method according to claim 1 further comprising the step of discarding series of measurements for which the differences between both measured values and their predicted values exceed a predetermined threshold for a predetermined period of time.

14. A method according to claim 1 in which the two independent measurements are made by impedance pneumography and photoplethysmography.

15. A method according to claim 1 in which there are more than two measurements.

16. A method according to claim 1 further comprising the step of identifying artefacts in the measurements based on the values of the differences between both measured values and their predicted values.

17. A computer program encoded on a computer-readable medium and comprising program code which, when executed, performs the method of claim 1.

18. Apparatus for measuring breathing rate of a subject, the apparatus comprising:

two independent measurement channels for receiving two independent measurements of a breathing rate as two measured values of the breathing rate; and

a processor configured to predict values of each of the two independent measurements of the breathing rate for the two independent measurement channels, to calculate respective differences between the predicted values and the measured values for each of said two independent measurement channels, and to combine the two measured values with weights determined by said differences.

19. The apparatus according to claim 18, further comprising:

a display for displaying the breathing rate.

20. Apparatus for measuring breathing rate of a subject, the apparatus comprising:

first and second independent measurement channels for providing respective independent measurements of a breathing rate as first and second measured values of the breathing rate; and

a processor configured to predict values of each of the first and second independent measurements of the breathing rate for the respective first and second independent measurement channels using respective Kalman filters, to calculate respective differences between the predicted values and the measured values for each of the first and second independent measurement channels, and to combine the first and second measured values with weights determined by the differences.