Method and apparatus for estimating a physiological parameter

• US 7,865,224 B2
• Filed: 10/12/2004
• Issued: 01/04/2011
• Est. Priority Date: 08/07/1995
• Status: Expired due to Fees

• Current Assignee: Nellcor Puritan Bennett LLC (Medtronic Plc)
• Original Assignee: Nellcor Puritan Bennett LLC (Medtronic Plc)
• Inventors: Yorkey, Thomas J., Baker, Clark R. Jr.
• Primary Examiner(s): WINAKUR, ERIC FRANK
• Application Number:
  US10/964,249
• Publication Number:
  US 20050085735A1
• Time in Patent Office: 2,275 Days
• Field of Search: 600/310, 600/322, 600/323, 600/324, 600/336, 600/500, 600/502
• US Class Current: 600/324
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/14552 Details of sensors specially adapted therefor
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: H03H 21/003 KALMAN filters

Abstract

In a physiological monitor, a method and an apparatus for determining a patient'"'"'s pulse rate using data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient. The method includes tracking the pulse rate in the data using an adaptive comb filter, the data having signal portions corresponding to the pulse rate and signal portions corresponding to noise, periodically calculating a frequency power spectrum of one of the wavelengths, and using the frequency power spectrum in a pulse rate calculator to determine the pulse rate or to verify the pulse rate calculated by the pulse rate calculator.

Claims

1. In a physiological monitor, a method for determining a patient'"'"'s pulse rate using data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient, the method comprising:

tracking the pulse rate in the data using an adaptive comb filter, said data having signal portions corresponding to the pulse rate and signal portions corresponding to noise;

periodically calculating a frequency power spectrum of one of said wavelengths; and

using the frequency power spectrum in a pulse rate calculator to determine the pulse rate or to verify the pulse rate calculated by the pulse rate calculator.

2. The method of claim 1 further comprising preprocessing the data by filtering the data to filter out frequencies that do not reflect cardiac blood flow.

3. The method of claim 1 further comprising preprocessing the data by taking the natural logarithm of the data and band pass filtering the data to attenuate the data below approximately 0.5 Hz and above approximately 10 Hz.

4. The method of claim 1 wherein said tracking is configured to track slowly varying heart rate.

5. The method of claim 1 wherein said calculating a frequency power spectrum comprises using a Fourier transform.

6. The method of claim 1 wherein said using a frequency power spectrum comprises determining which of a plurality of peaks in the frequency power spectrum corresponds to the fundamental frequency.

7. The method of claim 1 wherein said tracking comprises defining a comb filter to remove signal energy from the data corresponding to the fundamental frequency and harmonics thereof;

and

determining a particular harmonic frequency which minimizes noise energy at an output of the comb filter, the particular frequency corresponding with the fundamental frequency.

8. The method of claim 1 further comprising calculating the autocorrelation function of said wavelengths;

identifying peaks in the power spectrum and peaks in the autocorrelation function; and

determining the pulse rate as the frequency corresponding with a peak present in both the frequency power spectrum and the autocorrelation function.

9. The method of claim 1 further comprising

using a pattern matching rate calculator to calculate an average pulse rate period over a set number of data samples,

wherein the pattern matching rate calculator recognizes sequences of crests and troughs in the data and is configured to reject pulses that are potentially contaminated by motion artifact.

10. The method of claim 1 further comprising

using the pulse rate determined using the frequency power spectrum as triggers for a cardiac gated averaging processor configured for averaging the plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient to generate a plurality of filtered waveforms to render them substantially free of noise.

11. The method of claim 10 further comprising determining oxygen saturation of hemoglobin in arterial blood in the tissue of the patient in an oxygen saturation calculator using the filtered waveforms.

12. A physiological monitor for monitoring a pulse rate of a patient using data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient, said monitor comprising a processor configured to:

track the pulse rate in the data using an adaptive comb filter, said data having signal portions corresponding to the pulse rate and signal portions corresponding to noise;

periodically calculate a frequency power spectrum of one of said wavelengths; and

use the frequency power spectrum in a pulse rate calculator to determine the pulse rate or to verify the pulse rate calculated by the pulse rate calculator.

13. The physiological monitor of claim 12, wherein said processor is configured to preprocess the data to filter out frequencies that do not reflect cardiac blood flow.

14. The physiological monitor of claim 12, wherein said processor is configured to preprocess the data by taking the natural logarithm of the data and band pass filter the data to attenuate the data below approximately 0.5 Hz and above approximately 10 Hz.

15. The physiological monitor of claim 12, wherein said processor is configured to calculate a frequency power spectrum using a Fourier transform.

16. The physiological monitor of claim 12, wherein said processor is configured to use the frequency power spectrum to determine which of a plurality of peaks in the frequency power spectrum corresponds to the fundamental frequency.

17. The physiological monitor of claim 12, wherein said processor is configured to track the data by defining a comb filter to remove signal energy from the data corresponding to the fundamental frequency and harmonics thereof;

and

determine a particular harmonic frequency which minimizes noise energy at an output of the comb filter, the particular frequency corresponding with the fundamental frequency.

18. The physiological monitor of claim 12, wherein said processor is configured to calculate the autocorrelation function of said wavelengths;

identify peaks in the power spectrum and peaks in the autocorrelation function; and

determine the pulse rate as the frequency corresponding with a peak present in both the frequency power spectrum and the autocorrelation function.

19. The physiological monitor of claim 12, wherein said processor is configured to use a pattern matching rate calculator to calculate an average pulse rate period over a set number of data samples,

wherein the pattern matching rate calculator recognizes sequences of crests and troughs in the data and is configured to reject pulses that are potentially contaminated by motion artifact.

20. The physiological monitor of claim 12, wherein said processor is configured to use the pulse rate determined using the frequency power spectrum as triggers for a cardiac gated averaging processor configured for averaging the plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient to generate a plurality of filtered waveforms to render them substantially free of noise.

21. The physiological monitor of claim 20, wherein said processor is configured to determine oxygen saturation of hemoglobin in arterial blood in the tissue of the patient in an oxygen saturation calculator using the filtered waveforms.

22. In a physiological monitor, a method for determining a patient'"'"'s pulse rate using time-domain data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient, said data having signal portions corresponding to the pulse rate and signal portions corresponding to noise, the method comprising:

obtaining a first estimate of the pulse rate;

transforming the time-domain data into a spectral domain data;

identifying a plurality of spectral peaks at non-zero frequencies in said spectral domain data;

processing said plurality of spectral peaks in a pulse rate calculator to identify a fundamental frequency and one or more harmonics of the fundamental frequency; and

using one or more rules for verifying or resetting the first estimate of the pulse rate from said processing.

23. The method of claim 22 wherein said transforming comprises periodically calculating a frequency power spectrum of one of said wavelengths.

24. The method of claim 22 wherein said using one or more rules comprises selectively applying one or more of the rules based on a confidence measure associated with the first estimate of the pulse rate.

25. The method of claim 24 wherein said confidence measure is selected from a group of confidence measures consisting of:

a measure of the strength of the harmonic peaks in said spectral domain data, a signal to noise ratio in the time-domain data, a measure of the probability of arrhythmia, a measure of correlation amongst the plurality of the time-domain data, and combinations thereof.

26. The method of claim 22 wherein said plurality of spectral peaks comprise three or more spectral peaks.

27. A physiological monitor for monitoring a pulse rate of a patient using time-domain data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient, said monitor comprising a processor configured to:

obtain a first estimate of the pulse rate;

transform the time-domain data into a spectral domain data;

identify a plurality of spectral peaks at non-zero frequencies in said spectral domain data;

process said plurality of spectral peaks in a pulse rate calculator to identify a fundamental frequency and one or more harmonics of the fundamental frequency; and

use one or more rules to verify or reset the first estimate of the pulse rate from said processing.

28. The physiological monitor of claim 27, wherein said processor is configured to transform the time-domain data by periodically calculating a frequency power spectrum of one of said wavelengths.

29. The physiological monitor of claim 27, wherein said processor is configured to use one or more rules by selectively applying one or more of the rules based on a confidence measure associated with the first estimate of the pulse rate.

30. The physiological monitor of claim 29 wherein said confidence measure is selected from a group of confidence measures consisting of:

a measure of the strength of the harmonic peaks in said spectral domain data, a signal to noise ratio in the time-domain data, a measure of the probability of arrhythmia, a measure of correlation amongst the plurality of the time-domain data, and combinations thereof.

31. In a physiological monitor, a method for determining a patient'"'"'s pulse rate using time-domain data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient, said data having signal portions corresponding to a plethysmographic waveform and signal portions corresponding to noise, the method comprising:

transforming one or more of the plurality of wavelengths of the time-domain data into a spectral domain data to obtain a frequency power spectrum;

classifying spectral lines in the frequency power spectrum into spectral values corresponding to a fundamental frequency and one or more harmonics of the fundamental frequency; and

estimating the pulse rate from the spectral values using one or more rules to obtain an estimated pulse rate, wherein said using one or more rules comprises selectively applying one or more of the rules based on a confidence measure associated with the estimated pulse rate.

32. The method of claim 31 wherein said transforming comprises periodically calculating a frequency power spectrum for the time-domain data.

33. The method of claim 31 wherein said confidence measure is selected from a group of confidence measures consisting of:

a measure of the strength of the harmonic peaks in said spectral domain data, a signal to noise ratio in the time-domain data, a measure of the probability of arrhythmia, a measure of correlation amongst the plurality of the time-domain data, and combinations thereof.

34. In a physiological monitor, a method for determining a patient'"'"'s pulse rate using time-domain data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient, said data having signal portions corresponding to a plethysmographic waveform and signal portions corresponding to noise, the method comprising:

transforming one or more of the plurality of wavelengths of the time-domain data into a spectral domain data to obtain a frequency power spectrum;

classifying spectral lines in the frequency power spectrum into spectral values corresponding to a fundamental frequency and one or more harmonics of the fundamental frequency; and

estimating the pulse rate from the spectral values using one or more rules to obtain an estimated pulse rate,

wherein estimating the pulse rate further includes [by] tracking the pulse rate in the time-domain data using an adaptive comb filter.

35. A physiological monitor for monitoring a pulse rate of a patient using time-domain data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient, said monitor comprising a processor configured to:

transform one or more of the plurality of wavelengths of the time-domain data into a spectral domain data to obtain a frequency power spectrum;

classify spectral lines in the frequency power spectrum into spectral values corresponding to a fundamental frequency and one or more harmonics of the fundamental frequency; and

estimate the pulse rate from the spectral values using one or more rules to obtain an estimated pulse rate by selectively applying one or more of the rules based on a confidence measure associated with the estimated pulse rate.

36. The physiological monitor of claim 35, wherein said processor is configured to transform the time-domain data by periodically calculating a frequency power spectrum of said time-domain data.

37. The physiological monitor of claim 35 wherein said confidence measure is selected from a group of confidence measures consisting of:

a measure of the strength of the harmonic peaks in said spectral domain data, a signal to noise ratio in the time-domain data, a measure of the probability of arrhythmia, a measure of correlation amongst the plurality of the time-domain data, and combinations thereof.

38. A physiological monitor for monitoring a pulse rate of a patient using time-domain data corresponding to a plurality of wavelengths of electromagnetic energy transmitted through the tissue of the patient, said monitor comprising a processor configured to:

transform one or more of the plurality of wavelengths of the time-domain data into a spectral domain data to obtain a frequency power spectrum;

classify spectral lines in the frequency power spectrum into spectral values corresponding to a fundamental frequency and one or more harmonics of the fundamental frequency; and

estimate the pulse rate from the spectral values using one or more rules to obtain an estimated pulse rate,

wherein the estimate the pulse rate processing further includes tracking the pulse rate in the time-domain data using an adaptive comb filter.