Method, apparatus and system for removing motion artifacts from measurements of bodily parameters

• US 6,810,277 B2
• Filed: 08/06/2002
• Issued: 10/26/2004
• Est. Priority Date: 10/15/1998
• Status: Expired due to Fees

• Current Assignee: Philips Electronics North America Corporation (Koninklijke Philips N.V.)
• Original Assignee: RIC INVESTMENTS LLC (Respironics Incorporated)
• Inventors: DelFavero, John R., Allo, August J. Jr., Edgar, Reuben W. Jr., Martin, Jesus D., Jaffe, Michael B., Gunneson, Paul B.
• Primary Examiner(s): Jones, Mary B.
• Assistant Examiner(s): Kremer, Matthew J
• Application Number:
  US10/213,140
• Publication Number:
  US 20030009091A1
• Time in Patent Office: 812 Days
• Field of Search: 600/309-311, 600/322-326, 600/330-331, 600/336, 600/473, 600/476
• US Class Current: 600/336
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms

Abstract

A method for removing motion artifacts from devices for sensing bodily parameters and apparatus and system for effecting same. The method includes analyzing segments of measured data representing bodily parameters and possibly noise from motion artifacts. Each segment of measured data may correspond to a single light signal transmitted and detected after transmission or reflection through bodily tissue. Each data segment is frequency analyzed to determine up to three candidate peaks for further analysis. Each of the up to three candidate frequencies may be filtered and various parameters associated with each of the up to three candidate frequencies are calculated. The best frequency, if one exists, is determined by arbitrating the candidate frequencies using the calculated parameters according to predefined criteria. If a best frequency is found, a pulse rate and SpO₂ may be output. If a best frequency is not found, other, conventional techniques for calculating pulse rate and SpO₂ may be used. The above method may be applied to red and infrared pulse oximetry signals prior to calculating pulse rate and/or pulsatile blood oxygen concentration. Apparatus and systems disclosed are configured to perform methods disclosed according to the invention.

Claims

1. A method of removing motion artifacts from electrical signals representative of attenuated light signals, comprising:

transforming the electrical signals into frequency domain data;

identifying a plurality of candidate peaks from the frequency domain data, wherein the identifying includes eliminating harmonic frequencies from the plurality of candidate peaks such that no two of the plurality of candidate peaks comprise harmonics of one another;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters; and

arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters to select a best frequency.

2. The method of claim 1, further comprising conditioning the electrical signals to reduce spectral leakage prior to the transforming step.

3. The method of claim 2, wherein the conditioning includes filtering the electrical signals.

4. The method of claim 3, wherein the filtering is performed with a Hanning window.

5. The method of claim 1, wherein the transforming the electrical signals into frequency domain data is performed with a fast Fourier transform.

6. The method of claim 1, wherein the transforming the electrical signals into frequency domain data is performed with a technique selected from the group consisting of a periodogram, a correlogram, autoregressive methods, Prony'"'"'s method, minimum variance methods, maximum likelihood methods, a discrete cosine transform, a wavelet transform, a discrete Hartley transform and a Gabor transform.

7. The method of claim 1, wherein the identifying the plurality of candidate peaks further comprises:

assigning a largest power amplitude from the frequency domain data as a primary candidate peak;

assigning a next largest power amplitude as a secondary candidate peak; and

assigning a previous non-zero pulse rate as a tertiary candidate peak if the previous non-zero pulse rate is neither the primary candidate peak nor the secondary candidate peak.

8. The method of claim 1, wherein the identifying the plurality of candidate peaks further comprises identifying n peaks, by frequency, F₁ to F_n, in descending order of peak amplitude.

9. The method of claim 1, wherein the narrow band pass filtering comprises finite impulse response filtering.

10. The method of claim 1, wherein the narrow band pass filtering comprises infinite impulse response filtering.

11. The method of claim 1, wherein the narrow band pass filtering comprises filtering each of the plurality of candidate peaks with one of n narrow band filters to mask influence of candidate frequencies not under evaluation.

12. The method of claim 11, wherein n=8 and wherein each of the eight narrow band filters is separated by a fixed difference in center frequency in a range of approximately 25 bpm to approximately 30 bpm.

13. The method of claim 11, wherein each of the n narrow band filters is separated by a variable difference in center frequency in a range of approximately 25 bpm to approximately 30 bpm.

14. The method of claim 1, wherein the narrow band pass filtering comprises filtering each of the plurality of candidate peaks with a narrow band filter of variable center frequency.

15. The method of claim 1, wherein the narrow band pass filtering comprises filtering each of the plurality of candidate peaks with a narrow band filter wherein filter coefficients are generated and adjusted so that a center frequency of the narrow band filter is approximately a center frequency associated with each of the candidate peaks.

16. The method of claim 1, wherein the narrow band pass filtering comprises filtering each of the plurality of candidate peaks using a fast Fourier transform (FFT), narrow band filter and inverse FFT.

17. The method of claim 1, wherein the analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters includes calculating a window pulse rate.

18. The method of claim 1, wherein the analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters includes calculating pulse width variability.

19. The method of claim 1, wherein the analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters includes calculating SpO₂ variability.

20. The method of claim 1, wherein the analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters includes calculating pulse window SpO₂.

21. The method of claim 1, wherein the analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters includes calculating pulse rate history percentage.

22. The method of claim 1, wherein the analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters includes calculating pulse window confidence.

23. The method of claim 22, wherein the calculating pulse window confidence includes calculating a weighted sum of pulse width variability, SpO₂ variability and pulse rate history percentage.

24. The method of claim 1, wherein the analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters includes calculating a window pulse rate, pulse width variability, SpO₂ variability, pulse window SpO₂, pulse rate history percentage and pulse window confidence.

25. The method of claim 1, wherein the arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters includes applying a predetermined criteria to select the best frequency.

26. The method of claim 1, wherein the plurality of candidate peaks comprises up to three candidate peaks, including a primary candidate peak, a secondary candidate peak and a tertiary candidate peak.

27. The method of claim 26, wherein the arbitrating between at least some of the candidate peaks includes applying the following criteria using at least some of the developed parameters to select the best frequency:

if a primary candidate peak frequency is zero, then there is no valid candidate peak;

if a tertiary candidate peak pulse window confidence is less than a pulse window confidence for either the primary candidate peak or the secondary candidate peak, then the tertiary candidate peak is the best frequency;

if the primary candidate peak and the secondary candidate peak have both been rejected, then there is no valid candidate peak;

if the primary candidate peak has not been rejected and the secondary candidate peak has been rejected, then the primary candidate peak is the best frequency;

if the primary candidate peak has been rejected and the secondary candidate peak has not been rejected, then the secondary candidate peak is the best frequency;

if the primary candidate peak pulse window confidence is greater than the secondary candidate peak pulse window confidence by a first threshold, t1, and the primary candidate peak pulse rate history percentage is greater than a second threshold, t2, then the primary candidate peak is the best frequency;

if the secondary candidate peak frequency is a rough harmonic of the primary candidate peak frequency and the pulse window confidence of the primary candidate peak is not more than a specified number of points greater than the pulse window confidence of the secondary candidate peak, then accept the primary candidate peak; and

if the pulse window confidence of the primary candidate peak is no more than a specified number of points greater than the pulse window confidence of the secondary candidate peak, then the primary candidate peak is the best frequency, otherwise, the secondary candidate peak is the best frequency.

28. The method of claim 27, wherein the arbitration is conducted in the sequence presented in claim 27.

29. A method of determining pulse rate and blood oxygen saturation from electrical signals representative of attenuated light signals and motion artifacts, comprising:

acquiring a segment of red data and a segment of IR data from each of the electrical signals representative of attenuated light signals;

transforming both the segment of red data and the segment of IR data into red and IR frequency domain data, respectively;

identifying a plurality of candidate peaks from the red and IR frequency domain data, wherein the identifying includes eliminating harmonic frequencies from the plurality of candidate peaks such that no two of the plurality of candidate peaks comprise harmonics of one another;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters;

arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters to select a best frequency;

outputting pulse rate and blood oxygen saturation relating to the best frequency; and

repeating the above steps for new segments of data.

30. The method of claim 29, wherein the transforming includes performing a fast Fourier transform.

31. The method of claim 29, wherein the identifying the plurality of candidate peaks comprises:

assigning a largest power amplitude from the red and IR frequency domain data as a primary candidate peak;

assigning a next largest power amplitude as a secondary candidate peak;

assigning a previous non-zero pulse rate as a tertiary candidate peak if the previous non-zero pulse rate is neither the primary candidate peak nor the secondary candidate peak.

32. The method of claim 29, wherein the developed parameters include pulse width variability calculated as a sum of absolute differences between individual pulse widths and an average pulse width normalized by the average pulse width.

33. The method of claim 29, wherein the developed parameters include SpO₂ variability calculated as a sum of absolute difference between individual SpO₂ values and an average SpO₂ for a given pulse window.

34. The method of claim 29, wherein the developed parameters include pulse window SpO₂ calculated by taking a measure of central tendency of all individual SpO₂ calculations in a given pulse window.

35. The method of claim 29, wherein the developed parameters include pulse peak amplitude variability calculated as a sum of differences between individual pulse peak amplitudes and average pulse peak amplitude for a given pulse window.

36. The method of claim 29, wherein the developed parameters include pulse rate history percentage calculated as a percentage of time that a pulse rate corresponding to a candidate peak has occurred in a given period of time.

37. The method of claim 29, wherein the developed parameters include pulse window confidence calculated as a weighted sum of pulse width variability, SpO₂ variability, pulse amplitude variability and pulse rate history percentage.

38. The method of claim 29, wherein the arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters includes applying selection criteria to select the best frequency.

39. A method of determining pulse rate and blood oxygen saturation from electrical signals representative of attenuated light signals and motion artifacts, comprising:

acquiring a segment of red data and a segment of IR data from each of the electrical signals representative of attenuated light signals;

transforming both the segment of red data and the segment of IR data into red and IR frequency domain data, respectively;

identifying a plurality of candidate peaks from the red and IR frequency domain data;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks wherein the developed parameters include window pulse rate calculated by dividing a sum of all pulse width times of all peaks in a data segment by quantity of peaks detected in the data segment;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters;

arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters to select a best frequency;

outputting pulse rate and blood oxygen saturation relating to the best frequency; and

repeating the above steps for new segments of data.

40. The method of claim 39, wherein the transforming includes performing a fast Fourier transform.

41. The method of claim 39, wherein the identifying the plurality of candidate peaks comprises:

assigning a largest power amplitude from the red and IR frequency domain data as a primary candidate peak;

assigning a next largest power amplitude as a secondary candidate peak; and

assigning a previous non-zero pulse rate as a tertiary candidate peak if the previous non-zero pulse rate is neither the primary candidate peak nor the secondary candidate peak.

42. The method of claim 39, wherein the developed parameters include pulse width variability calculated as a sum of absolute differences between individual pulse widths and an average pulse width normalized by the average pulse width.

43. The method of claim 39, wherein the developed parameters include SpO₂ variability calculated as a sum of absolute difference between individual SpO₂ values and an average SpO₂ for a given pulse window.

44. The method of claim 39, wherein the developed parameters include pulse window SpO₂ calculated by taking a measure of central tendency of all individual SpO₂ calculations in a given pulse window.

45. The method of claim 39, wherein the developed parameters include pulse peak amplitude variability calculated as a sum of differences between individual pulse peak amplitudes and average pulse peak amplitude for a given pulse window.

46. The method of claim 39, wherein the developed parameters include pulse rate history percentage calculated as a percentage of time that a pulse rate corresponding to a candidate peak has occurred in a given period of time.

47. The method of claim 39, wherein the developed parameters include pulse window confidence calculated as a weighted sum of pulse width variability, SpO₂ variability, pulse amplitude variability and pulse rate history percentage.

48. The method of claim 39, wherein the arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters includes applying selection criteria to select the best frequency.

49. A circuit card for use in a pulse oximetry system to remove motion-induced noise artifacts from attenuated light signals, the circuit card comprising:

a circuit board for mounting electronic circuitry and interfacing with the pulse oximetry system;

a processor mounted on the circuit board for processing input signals according to instructions; and

a memory storing a computer program, wherein the memory is operably coupled to the processor, and wherein the computer program includes instructions for implementing a method of removing motion artifacts from the attenuated light signals, the method comprising;

acquiring a segment of red data and a segment of IR data from the attenuated light signals to obtain electrical signals representative of the attenuated light signals;

conditioning the electrical signals to reduce spectral leakage;

transforming the electrical signals into frequency domain data;

identifying a plurality of candidate peaks from the frequency domain data, wherein the identifying includes eliminating harmonic frequencies from the plurality of candidate peaks such that no two of the plurality of candidate peaks comprise harmonics of one another;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters;

arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters to select a best frequency; and

repeating the above steps with a new segment of data.

50. The circuit card of claim 49, wherein the processor is a digital signal processor.

51. The circuit card of claim 49, further configured for calculating pulsatile blood oxygen concentration, SpO₂, using the best frequency.

52. The circuit card of claim 49, further configured for calculating pulse rate using the best frequency.

53. A pulse oximeter for removing motion-induced noise artifacts from electrical signals representative of attenuated light signals comprising an input device, an output device, and a motion artifact circuit card, wherein the motion artifact circuit card comprises:

a circuit board for mounting electronic circuitry and interfacing with the pulse oximeter;

a processor mounted on the circuit board for processing at least one input signal according to instructions; and

a memory storing a computer program, wherein the memory is operably coupled to the processor, and wherein the computer program includes instructions for implementing a method of removing motion artifacts from the attenuated light signals, the method comprising;

acquiring a segment of red data and a segment of IR data from the attenuated light signals to obtain the electrical signals representative of the attenuated light signals;

conditioning the electrical signals to reduce spectral leakage;

transforming the electrical signals into frequency domain data;

identifying a plurality of candidate peaks from the frequency domain data, wherein the identifying includes eliminating harmonic frequencies from the plurality of candidate peaks such that no two of the plurality of candidate peaks comprise harmonics of one another;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters;

arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters to select a best frequency; and

repeating the above steps with a new segment of data.

54. The pulse oximeter of claim 53, wherein the processor is a digital signal processor.

55. A pulse oximetry system for removing motion-induced noise artifacts from electrical signals representative of attenuated light signals comprising an input device, an output device, and motion artifact circuitry, wherein the motion artifact circuitry includes:

a processor for processing at least one input signal according to instructions; and

a memory operably coupled to the processor storing a computer program, wherein the computer program includes instructions for implementing a method of removing motion artifacts from the attenuated light signals, wherein the method comprises;

acquiring a segment of red data and a segment of IR data from the attenuated light signals to obtain the electrical signals representative of the attenuated light signals;

conditioning the electrical signals to reduce spectral leakage;

transforming the electrical signals into frequency domain data;

identifying a plurality of candidate peaks from the frequency domain data, wherein the identifying includes eliminating harmonic frequencies from the plurality of candidate peaks such that no two of the plurality of candidate peaks comprise harmonics of one another;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters;

arbitrating between at least some of the plurality of candidate peaks using at least some of the developed parameters to select a best frequency;

outputting pulse rate and saturation relating to the best frequency; and

repeating the above steps for new segments of data.

56. A circuit card for use in a pulse oximetry system to remove motion-induced noise artifacts from attenuated light signals, the circuit card comprising:

a circuit board for mounting electronic circuitry and interfacing with the pulse oximetry system;

a processor mounted on the circuit board for processing input signals according to instructions; and

a memory storing a computer program, wherein the memory is operably coupled to the processor, and wherein the computer program includes instructions for implementing a method of removing motion artifacts from the attenuated light signals, the method comprising;

acquiring a segment of red data and a segment of IR data from the attenuated light signals to obtain electrical signals representative of the attenuated light signals;

conditioning the electrical signals to reduce spectral leakage;

transforming the electrical signals into frequency domain data;

identifying a plurality of candidate peaks from the frequency domain data;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks, wherein the developed parameters include window pulse rate calculated by dividing a sum of all pulse width times of all peaks in a data segment by quantity of peaks detected in the data segment;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters;

arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters to select a best frequency; and

repeating the above steps with a new segment of data.

57. The circuit card of claim 56, wherein the processor is a digital signal processor.

58. The circuit card of claim 56, further configured for calculating pulsatile blood oxygen concentration, SpO₂, using the best frequency.

59. The circuit card of claim 56, further configured for calculating pulse rate using the best frequency.

60. A pulse oximeter for removing motion-induced noise artifacts from electrical signals representative of attenuated light signals comprising an input device, an output device, and a motion artifact circuit card, wherein the motion artifact circuit card comprises:

a circuit board for mounting electronic circuitry and interfacing with the pulse oximeter;

a processor mounted on the circuit board for processing at least one input signal according to instructions; and

a memory storing a computer program, wherein the memory is operably coupled to the processor, and wherein the computer program includes instructions for implementing a method of removing motion artifacts from the attenuated light signals, the method comprising;

acquiring a segment of red data and a segment of IR data from the attenuated light signals to obtain the electrical signals representative of the attenuated light signals;

conditioning the electrical signals to reduce spectral leakage;

transforming the electrical signals into frequency domain data;

identifying a plurality of candidate peaks from the frequency domain data;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks, wherein the developed parameters include window pulse rate calculated by dividing a sum of all pulse width times of all peaks in a data segment by quantity of peaks detected in the data segment;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters;

arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters to select a best frequency; and

repeating the above steps with a new segment of data.

61. The pulse oximeter of claim 60, wherein the processor is a digital signal processor.

62. A pulse oximetry system for removing motion-induced noise artifacts from electrical signals representative of attenuated light signals comprising an input device, an output device, and motion artifact circuitry, wherein the motion artifact circuitry includes:

a processor for processing at least one input signal according to instructions; and

a memory operably coupled to the processor storing a computer program, wherein the computer program includes instructions for implementing a method of removing motion artifacts from the attenuated light signals, wherein the method comprises;

acquiring a segment of red data and a segment of IR data from the attenuated light signals to obtain the electrical signals representative of the attenuated light signals;

conditioning the electrical signals to reduce spectral leakage;

transforming the electrical signals into frequency domain data;

identifying a plurality of candidate peaks from the frequency domain data;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks, wherein the developed parameters include window pulse rate calculated by dividing a sum of all pulse width times of all peaks in a data segment by quantity of peaks detected in the data segment;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters;

arbitrating between at least some of the plurality of candidate peaks using at least some of the developed parameters to select a best frequency;

outputting pulse rate and saturation relating to the best frequency; and

repeating the above steps for new segments of data.

63. A method of removing motion artifacts from electrical signals representative of attenuated light signals, comprising:

transforming the electrical signals into frequency domain data;

identifying a plurality of candidate peaks from the frequency domain data;

narrow band pass filtering at each of the plurality of candidate peaks;

developing parameters associated with each of the plurality of candidate peaks, wherein the developed parameters include window pulse rate calculated by dividing a sum of all pulse width times of all peaks in a data segment by quantity of peaks detected in the data segment;

analyzing each of the plurality of candidate peaks with respect to at least some of the developed parameters; and

arbitrating between at least some of the plurality of candidate peaks employing at least some of the developed parameters to select a best frequency.