SIGNAL PROCESSING FOR PULSE OXIMETRY

• US 20110270059A1
• Filed: 07/13/2011
• Published: 11/03/2011
• Est. Priority Date: 09/30/2005
• Status: Abandoned Application

• Current Assignee: Conmed Corporation (Jefferies Financial Group, Inc.)
• Original Assignee: Conmed Corporation (Jefferies Financial Group, Inc.)
• Inventors: Lowery, Guy Russell, Wasserman, Yoram
• Application Number:
  US13/182,340
• Publication Number:
  US 20110270059A1
• Time in Patent Office: Days
• US Class Current: 600/324
• 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 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/14551 for measuring blood gases

Abstract

A signal processing technique for estimating the frequency of a pulsatile signal (including but not limited to pulse oximetry signals) is disclosed. Each of the functions contained within a pre-selected set of functions is compared to the input signal at many different time-shifts, and the function/time-shift combination that best matches the input signal is selected. The frequency of the best-matching function is then used as the best estimate of the frequency of the input signal. Optionally, once a function has been selected, the rising portion of the selected function can be correlated in time to the rising portion of the input signal. Improved results can then be obtained by basing the oxygen saturation level calculations on samples taken from the rising portion of the input signal.

Claims

1. A method of determining the pulse rate of a living subject from a pulse oximetry input signal, the method comprising the steps of:

comparing the input signal at each of a plurality of different time shifts to each member of a set of functions, wherein the set includes functions with a plurality of different frequencies between 30 to 240 cycles per minute;

selecting, based on results obtained in the comparing step, a function from the set that best matches the input signal; and

using the frequency of the selected function as an estimate of the pulse rate of the subject.

2. The method of claim 1, further comprising the step of removing DC and very low frequency components from the input signal, wherein the removing step is implemented before the comparing step.

3. The method of claim 1, wherein the functions in the set each have a duration between about 3 and about 5 seconds.

4. The method of claim 3, wherein the set contains at least 211 functions.

5. The method of claim 1, wherein the frequencies of the functions in the set are distributed in frequency to provide resolution of at least one cycle per minute.

6. The method of claim 1, wherein the set contains a first subset of functions in which the rise time is shorter than the fall time, and the shape of all the functions in the first subset is similar.

7. The method of claim 6, wherein the frequencies of the functions in the first subset are distributed in frequency to provide resolution of at least one cycle per minute.

8. The method of claim 6, wherein the set further contains a second subset of functions in which the rise time is about the same as the fall time, and the shape of all the functions in the second subset is similar.

9. The method of claim 8, wherein the frequencies of the functions in the first subset and the second subset are distributed in frequency to provide resolution of at least one cycle per minute.

10. The method of claim 9, wherein the set further contains a third subset of functions in which the rise time is longer than the fall time, and the shape of all the functions in the third subset is similar.

11. The method of claim 1, wherein the comparing and selecting steps are implemented based on the formula

F=MAX_i,j{a₁*f₁(S_—

PFFj(t),I(t−

i))+a₂*f₂(S-PFFj(t),I(t−

i))+a₃*f₃(S-PFFj(t),I(t−

i)) . . . +a_n*f_n(S-PFFj(t),I(t−

i))}

where;

f₁-f_n are a set of n linear or non-linear functions;

S-PFF is a set of pre-defined frequency functions, as detailed above;

j is an index indicating a selected S-PFF function from the N S-PFF functions defined (note that n is independent of N);

a₁-a_n are selected weighting constants

* is the multiplication operator;

I is the input signal;

t is an index indicating a specific sample of S-PFFj and I (i.e. these functions at time t); and

i is a selected shift value, which indicates the current shift between the function S-PFFj and the input signal I.

12. The method of claim 1, wherein the comparing and selecting steps are implemented based on the formula

F=MAX_i,j{f₁(S-PFFj(t),I(t−

i))−

f₂(S-PFFj(t),I(t−

i))−

f₃(S-PFFj(t),I(t−

i))},

where;

the function f₁(i, j) is the sum of the products, obtained by multiplying the functions S-PFFj and input signal I, while shifted by i samples, resulting in the equation f₁(i,j)=Σ

_t S-PFFj(t)*I(t−

i);

the function f₂(i, j) is the sum of differences between the consecutive values I(t_k) of the input signal I, where the values {t_k} represent the indexes of the maxima of function S-PFFj(t);

the function f₃(i, j) is the sum of differences between the consecutive values I(t_m), of the input signal, where the values {t_m} represent the indexes of the minima of function S-PFFj(t);

S-PFF is a set of pre-defined frequency functions;

j is an index indicating a selected S-PFF function from the N S-PFF functions defined;

I is the input signal;

t is an index indicating a specific sample of S-PFFj and I (i.e. these functions at time t); and

i is a selected shift value, which indicates the current shift between the function S-PFFj and the input signal I.

13. A method of determining the frequency of a pulsatile input signal, the method comprising the steps of:

comparing the input signal at each of a plurality of different time shifts to each member of a set of functions, wherein the set includes functions with a plurality of different frequencies in a frequency range that is expected for the input signal;

selecting, based on results obtained in the comparing step, a function from the set that best matches the input signal; and

using the frequency of the selected function as an estimate of the frequency of a input signal.

14. The method of claim 13, further comprising the step of removing DC and very low frequency components from the input signal, wherein the removing step is implemented before the comparing step.

15. The method of claim 13, wherein the set contains a first subset of functions, and the shape of all the functions in the first subset is similar.

16. The method of claim 15, wherein the set contains a second subset of functions, and the shape of all the functions in the second subset is similar.

17. The method of claim 13, wherein the comparing and selecting steps are implemented based on the formula

F=MAX_i,j{a₁*f₁(S_—

PFFj(t),I(t−

i))+a₂*f₂(S-PFFj(t),I(t−

i))+a₃*f₃(S-PFFj(t),I(t−

i)) . . . +a_n*f_n(S-PFFj(t),I(t−

i))}

where;

f₁-f_n are a set of n linear or non-linear functions;

S-PFF is a set of pre-defined frequency functions, as detailed above;

j is an index indicating a selected S-PFF function from the N S-PFF functions defined (note that n is independent of N);

a₁-a_n are selected weighting constants

* is the multiplication operator;

I is the input signal;

t is an index indicating a specific sample of S-PFFj and I (i.e. these functions at time t); and

i is a selected shift value, which indicates the current shift between the function S-PFFj and the input signal I.