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

• US 7,991,448 B2
• Filed: 04/21/2006
• Issued: 08/02/2011
• Est. Priority Date: 10/15/1998
• Status: Expired due to Fees

• Current Assignee: Philips Electronics North America Corporation (Koninklijke Philips N.V.)
• Original Assignee: Philips Electronics North America Corporation (Koninklijke Philips N.V.)
• Inventors: DelFavero, John R., Jaffe, Michael B., Gunneson, Paul B., Allo, August J. Jr., Edgar, Reuben W. Jr., Martin, Jesus D.
• Primary Examiner(s): Winakur; Eric F
• Assistant Examiner(s): Liu; Chu Chuan
• Application Number:
  US11/408,604
• Publication Number:
  US 20060258927A1
• Time in Patent Office: 1,929 Days
• Field of Search: 600309-344, 356/39, 356/419
• 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: G06F 2218/04 Denoising

Abstract

A method for removing motion artifacts from devices for sensing bodily parameters and apparatus and system for effecting same that includes analyzing segments of measured data representing bodily parameters and possibly noise from motion artifacts. Each data segment is frequency analyzed to determine up to three candidate peaks for further analysis. Up to three candidate frequencies may be filtered and various parameters associated with each candidate frequency are calculated. A pulse-estimate input may also be accepted from an external source. The best frequency, if one exists, is determined by arbitrating the candidate frequencies and the pulse-estimate input 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.

Claims

1. A method of removing motion artifacts, comprising:

transforming a plurality of electrical signals representative of a plurality of attenuated light signals into frequency domain data;

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

eliminating candidate peaks from the plurality of candidate peaks such that no two of the plurality of candidate peaks comprise harmonics of one another;

filtering each of the plurality of candidate peaks;

accepting a pulse-estimate input;

developing selection parameters associated with each of the plurality of candidate peaks and the pulse-estimate input; and

selecting a best frequency from at least some of the plurality of candidate peaks and the pulse-estimate input by employing at least some of the selection parameters.

2. The method of claim 1, wherein selecting the best frequency comprises analyzing each of the plurality of candidate peaks and the pulse-estimate input with respect to at least some of the selection parameters to find the best frequency.

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

assigning a largest power amplitude 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.

4. The method of claim 1, wherein transforming the plurality of electrical signals is performed using a fast Fourier transform, a periodogram, a correlogram, an autoregressive methods, Prony'"'"'s method, minimum variance methods, maximum likelihood methods, a discrete cosine transform, a wavelet transform, a discrete Hartley transform, or a Gabor transform.

5. he method of claim 1, further comprising conditioning the plurality of electrical signals to reduce spectral leakage prior to the transforming act.

6. The method of claim 1, wherein the filtering comprises

using a narrow band filter;

using a narrow band filter with a variable center frequency;

using one of n narrow band filters to mask influence of candidate frequencies not under evaluation;

or

using 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.

7. The method of claim 1, wherein the selection parameters include at least one of:

pulse width variability;

SpO₂ variability;

pulse window SpO₂;

pulse peak amplitude variability;

pulse rate history percentage;

window pulse rate; and

pulse window confidence.

8. The method of claim 1, wherein the selection parameters include at least one of:

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;

SpO₂ variability calculated as a sum of absolute difference between individual SpO₂ values and an average SpO₂ for a given pulse window;

pulse window SpO₂ calculated by taking a measure of central tendency of all individual SpO₂ calculations in a given pulse window;

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;

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;

pulse window confidence calculated as a weighted sum of pulse width variability, SpO₂ variability, pulse peak amplitude variability and pulse rate history percentage; and

window pulse rate calculated by dividing a sum of all pulse width times of all peaks in a data segment by a quantity of peaks detected in the data segment.

9. A method of determining parameters related to blood flow, comprising:

acquiring a segment of red data by transforming an electrical signal representative of an attenuated red light signal;

acquiring a segment of infrared data by transforming an electrical signal representative of an attenuated infrared light signal;

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

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

eliminating harmonic frequencies from the plurality of candidate peaks such that no two of the plurality of candidate peaks comprise harmonics of one another;

filtering each of the plurality of candidate peaks;

accepting a pulse-estimate input;

developing selection parameters associated with each of the plurality of candidate peaks and the pulse-estimate input;

selecting a best frequency from at least some of the plurality of candidate peaks and the pulse-estimate input by employing at least some of the selection parameters; and

repeating the above acts for a new segment of red data and a new segment of infrared data.

10. The method of claim 9, further comprising outputting at least one of a pulse rate, SpO₂, and a blood oxygen saturation, relating to the best frequency.

11. The method of claim 9, wherein selecting the best frequency comprises analyzing each of the plurality of candidate peaks and the pulse-estimate input with respect to at least some of the selection parameters to find the best frequency.

12. The method of claim 9, wherein the identifying the plurality of candidate peaks comprises:

assigning a largest power amplitude 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.

13. The method of claim 9, wherein the selection parameters comprise at least one of:

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;

SpO₂ variability calculated as a sum of absolute difference between individual SpO₂ values and an average SpO₂ for a given pulse window;

pulse window SpO₂ calculated by taking a measure of central tendency of all individual SpO₂ calculations in a given pulse window;

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;

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;

pulse window confidence calculated as a weighted sum of pulse width variability, SpO₂ variability, pulse peak amplitude variability and pulse rate history percentage; and

window pulse rate calculated by dividing a sum of all pulse width times of all peaks in a data segment by a quantity of peaks detected in the data segment.

14. A motion artifact analyzer comprising:

(a) a processor for processing at least one computer program; and

(b) a memory operably coupled to the processor and storing the at least one computer program, the at least one computer program including instructions for implementing a method of removing motion artifacts, the method comprising;

(1) acquiring a segment of red data by transforming an electrical signal representative of an attenuated red light signal;

(2) acquiring a segment of infrared data by transforming an electrical signal representative of an attenuated infrared light signal;

(3) transforming the segment of red data and the segment of infrared data into red frequency domain data and infrared frequency domain data, respectively;

(4) identifying a plurality of candidate peaks from the red frequency domain data and the infrared frequency domain data;

(5) eliminating harmonic frequencies from the plurality of candidate peaks such that no two of the plurality of candidate peaks comprise harmonics of one another;

(6) filtering each of the plurality of candidate peaks;

(7) accepting a pulse-estimate input;

(8) developing selection parameters associated with each of the plurality of candidate peaks and the pulse-estimate input;

(9) selecting a best frequency from at least some of the plurality of candidate peaks and the pulse-estimate input by employing at least some of the selection parameters; and

(10) repeating the above acts for a new segment of red data and a new segment of infrared data.

15. The motion artifact analyzer of claim 14, wherein selecting the best frequency comprises analyzing each of the plurality of candidate peaks and the pulse-estimate input with respect to at least some of the selection parameters to find the best frequency.

16. The motion artifact analyzer of claim 14, wherein the processor is a digital signal processor.

17. The motion artifact analyzer of claim 14, wherein the method further comprises calculating and outputting at least one of pulsatile blood oxygen concentration, SpO₂, and pulse rate, using the best frequency.

18. The motion artifact analyzer of claim 14, further comprising an external source operably coupled to the processor and configured for providing the pulse-estimate input.

19. The motion artifact analyzer of claim 14, further comprising a pulse oximeter including an input device and an output device.

20. The motion artifact analyzer of claim 19, wherein the method further comprises calculating and outputting a pulsatile blood oxygen concentration, SpO₂, a pulse rate, or both using the best frequency.