Systems and methods for determining respiration information from a photoplethysmograph

• US 9,119,597 B2
• Filed: 09/23/2011
• Issued: 09/01/2015
• Est. Priority Date: 09/23/2011
• Status: Active Grant

• Current Assignee: Nellcor Puritan Bennett Ireland
• Original Assignee: Nellcor Puritan Bennett Ireland
• Inventors: Dripps, Jimmy, Ochs, James, Addison, Paul S., Watson, James
• Primary Examiner(s): Kahelin, Michael
• Assistant Examiner(s): TRAN, THO Q
• Application Number:
  US13/243,785
• Publication Number:
  US 20130079656A1
• Time in Patent Office: 1,439 Days
• Field of Search: 600/529
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence

Abstract

A signal representing physiological information may include information related to respiration. A patient monitoring system may generate a plurality of autocorrelation sequences from the signal and combine the autocorrelation sequences to generate a combined autocorrelation sequence. The combined autocorrelation sequence may be analyzed to identify one or more peaks that may correspond to respiration information. Respiration information such as respiration rate may be determined based on the one or more peaks.

Claims

1. A method for determining respiration rate, the method comprising:

receiving a photoplethysmograph (PPG) signal;

processing the PPG signal to generate a plurality of morphology signals, each of the morphology signals being indicative of a respective morphology of the PPG signal;

generating, using processing equipment, a plurality of autocorrelation sequences by generating a respective autocorrelation sequence for more than one of the plurality of morphology signals;

generating, using the processing equipment, a combined autocorrelation sequence from the plurality of autocorrelation sequences;

establishing a threshold associated with the combined autocorrelation sequence;

identifying a selected peak of the combined autocorrelation sequence based on the threshold; and

determining, using the processing equipment, a respiration rate from the combined autocorrelation sequence based on the selected peak.

2. The method of claim 1 wherein generating the combined autocorrelation sequence comprises:

calculating a combination weight for each autocorrelation sequence of the plurality of autocorrelation sequences; and

generating the combined autocorrelation sequence from the plurality of autocorrelation sequences based on the combination weights.

3. The method of claim 1 further comprising generating, using the processing equipment, a confidence value based on the combined autocorrelation sequence.

4. The method of claim 3 wherein the confidence value is based on a residual sum of a best fit of the combined autocorrelation sequence.

5. The method of claim 3 wherein the confidence value is based on a time distribution of the combined autocorrelation sequence.

6. A method for determining respiration rate, the method comprising:

receiving a photoplethysmograph (PPG) signal;

processing the PPG signal to generate a plurality of morphology signals, each of the morphology signals being indicative of a respective morphology of the PPG signal;

generating, using processing equipment, a plurality of autocorrelation sequences by generating a respective autocorrelation sequence for more than one of the plurality of morphology signals;

generating, using the processing equipment, a combined autocorrelation sequence from the plurality of autocorrelation sequences;

establishing a range of interest associated with the combined autocorrelation sequence;

identifying a selected peak based on the range of interest; and

determining, using the processing equipment, a respiration rate from the combined autocorrelation sequence based on the selected peak.

7. The method of claim 6 wherein generating the combined autocorrelation sequence comprises:

calculating a combination weight for each autocorrelation sequence of the plurality of autocorrelation sequences; and

generating the combined autocorrelation sequence from the plurality of autocorrelation sequences based on the combination weights.

8. The method of claim 6 further comprising generating, using the processing equipment, a confidence value based on the combined autocorrelation sequence.

9. The method of claim 8 wherein the confidence value is based on a residual sum of a best fit of the combined autocorrelation sequence.

10. The method of claim 8 wherein the confidence value is based on a time distribution of the combined autocorrelation sequence.

11. A method for determining respiration rate, the method comprising:

receiving a photoplethysmograph (PPG) signal;

processing the PPG signal to generate a plurality of morphology signals, each of the morphology signals being indicative of a respective morphology of the PPG signal;

generating, using processing equipment, aplurality of autocorrelation sequences by generating a respective autocorrelation sequence for more than one of the plurality of morphology signals;

generating, using the processing equipment, a combined autocorrelation sequence from the plurality of autocorrelation sequences;

identifying one or more peaks associated with the combined autocorrelation sequence;

determining whether any of the one or more peaks correspond to a harmonic;

identifying a selected peak from the one or more peaks, wherein the selected peak does not correspond to a harmonic; and

determining, using the processing equipment, a respiration rate from the combined autocorrelation sequence based on the selected peak.

12. The method of claim 11 wherein generating the combined autocorrelation sequence comprises:

calculating a combination weight for each autocorrelation sequence of the plurality of autocorrelation sequences; and

generating the combined autocorrelation sequence from the plurality of autocorrelation sequences based on the combination weights.

13. The method of claim 11 further comprising generating, using the processing equipment, a confidence value based on the combined autocorrelation sequence.

14. The method of claim 13 wherein the confidence value is based on a residual sum of a best fit of the combined autocorrelation sequence.

15. The method of claim 13 wherein the confidence value is based on a time distribution of the combined autocorrelation sequence.

16. A patient monitoring system comprising:

an interface configured to receive a photoplethysmograph (PPG) signal; and

processing equipment configured to;

process the PPG signal to generate a plurality of morphology signals, each of the morphology signals being indicative of a respective morphology of the PPG signal;

generate a plurality of autocorrelation sequences by generating a respective autocorrelation sequence for more than one of the plurality of morphology signals,

generate a combined autocorrelation sequence from the plurality of autocorrelation sequences,

establish a threshold associated with the combined autocorrelation sequence,

identify a selected peak of the combined autocorrelation sequence based on the threshold, and

determine a respiration rate from the combined autocorrelation sequence based on the selected peak.

17. The patient monitoring system of claim 16 wherein the processing equipment is further configured to calculate a combination weight for each autocorrelation sequence of the plurality of autocorrelation sequences and to generate the combined autocorrelation sequence from the plurality of autocorrelation sequences based on the combination weights.

18. The patient monitoring system of claim 16 wherein the processing equipment is further configured to generate a confidence value based on the combined autocorrelation sequence.

19. The patient monitoring system of claim 18 wherein the confidence value is based on a residual sum of a best fit of the combined autocorrelation sequence.

20. The patient monitoring system of claim 18 wherein the confidence value is based on a time distribution of the combined autocorrelation sequence.

21. A patient monitoring system comprising:

an interface configured to receive a photoplethysmograph (PPG) signal; and

processing equipment configured to;

process the PPG signal to generate a plurality of morphology signals, each of the morphology signals being indicative of a respective morphology of the PPG signal;

generate a plurality of autocorrelation sequences by generating a respective autocorrelation sequence for more than one of the plurality of morphology signals,

generate a combined autocorrelation sequence from the plurality of autocorrelation sequences,

establish a range of interest associated with the combined autocorrelation sequence;

identify a selected peak based on the range of interest; and

determine a respiration rate from the combined autocorrelation sequence based on the selected peak.

22. The patient monitoring system of claim 21 wherein the processing equipment is further configured to calculate a combination weight for each autocorrelation sequence of the plurality of autocorrelation sequences and to generate the combined autocorrelation sequence from the plurality of autocorrelation sequences based on the combination weights.

23. The patient monitoring system of claim 21 wherein the processing equipment is further configured to generate a confidence value based on the combined autocorrelation sequence.

24. The patient monitoring system of claim 23 wherein the confidence value is based on a residual sum of a best fit of the combined autocorrelation sequence.

25. The patient monitoring system of claim 23 wherein the confidence value is based on a time distribution of the combined autocorrelation sequence.

26. A patient monitoring system comprising:

an interface configured to receive a photoplethysmograph (PPG) signal; and

processing equipment configured to;

process the PPG signal to generate a plurality of morphology signals, each of the morphology signals being indicative of a respective morphology of the PPG signal;

generate a plurality of autocorrelation sequences by generating a respective autocorrelation sequence for more than one of the plurality of morphology signals,

generate a combined autocorrelation sequence from the plurality of autocorrelation sequences,

identify one or more peaks associated with the combined autocorrelation sequence;

determine whether any of the one or more peaks correspond to a harmonic;

identify a selected peak from the one or more peaks, wherein the selected peak does not correspond to a harmonic; and

determine a respiration rate from the combined autocorrelation sequence based on the selected peak.

27. The patient monitoring system of claim 26 wherein the processing equipment is further configured to calculate a combination weight for each autocorrelation sequence of the plurality of autocorrelation sequences and to generate the combined autocorrelation sequence from the plurality of autocorrelation sequences based on the combination weights.

28. The patient monitoring system of claim 26 wherein the processing equipment is further configured to generate a confidence value based on the combined autocorrelation sequence.

29. The patient monitoring system of claim 28 wherein the confidence value is based on a residual sum of a best fit of the combined autocorrelation sequence.

30. The patient monitoring system of claim 28 wherein the confidence value is based on a time distribution of the combined autocorrelation sequence.