Systems and methods for determining respiration information from a photoplethysmograph

• US 8,880,576 B2
• Filed: 09/23/2011
• Issued: 11/04/2014
• Est. Priority Date: 09/23/2011
• Status: Active Grant

• Current Assignee: Nellcor Puritan Bennett Ireland
• Original Assignee: Nellcor Puritan Bennett Ireland
• Inventors: Ochs, James, Addison, Paul S., Watson, James
• Primary Examiner(s): Mai, Tan V.
• Application Number:
  US13/243,951
• Publication Number:
  US 20130080489A1
• Time in Patent Office: 1,138 Days
• Field of Search: 708/426
• US Class Current: 708/426
• 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 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 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 5/7203 for noise prevention, reduction or removal

Abstract

A patient monitoring system may receive a photoplethysmograph (PPG) signal including samples of a pulse waveform. A plurality of morphology metric signals may be generated from the PPG signal. The system may generate an autocorrelation sequence for each of the morphology metric signals. An autocorrelation metric may be generated from each of the autocorrelation sequences and may represent the regularity or periodicity of the morphology metric signal. The autocorrelation sequences may be combined to generate a combined autocorrelation sequence, with the weighting of the autocorrelation sequences based on the autocorrelation metric. The combined autocorrelation sequence may be used to determine physiological information.

Claims

1. A method for determining physiological information, the method comprising:

generating, using processing equipment, a plurality of morphology metric signals based on a photoplethysmograph (PPG) signal;

generating, using the processing equipment, a plurality of autocorrelation sequences, wherein each autocorrelation sequence is derived from a respective one of the morphology metric signals; and

determining, using the processing equipment, the physiological information from the plurality of autocorrelation sequences.

2. The method of claim 1 wherein the plurality of morphology metric signals comprises a down metric signal, a kurtosis metric signal, and a DSD metric signal.

3. The method of claim 1 further comprising generating a plurality of autocorrelation metrics using the processing equipment, wherein each autocorrelation metric corresponds to one of the autocorrelation sequences.

4. The method of claim 3 wherein generating each of the plurality of autocorrelation metrics comprises:

identifying a plurality of local minima points for a corresponding autocorrelation sequence;

normalizing the local minima based on a peak of the corresponding autocorrelation sequence;

averaging the magnitudes of the normalized local minima points; and

generating the autocorrelation metric based on the averaging.

5. The method of claim 3 wherein generating each autocorrelation sequence further comprises:

generating an autocorrelation of the respective morphology metric signal;

calculating a ratio of the usable portion of the PPG signal;

calculating a filter weight based on the ratio and the autocorrelation metric; and

filtering the autocorrelation based on the filter weight to generate the autocorrelation sequence.

6. The method of claim 5 further comprising calculating a sequence age for each of the autocorrelation sequences based on the filter weight, a current age for the autocorrelation sequence, and a previous age for the autocorrelation sequence.

7. The method of claim 1 wherein determining the physiological information comprises:

combining the plurality of autocorrelation sequences to generate a combined autocorrelation sequence; and

determining the physiological information based on the combined autocorrelation sequence.

8. The method of claim 7 wherein combining the plurality of autocorrelation sequences comprises:

calculating a plurality of combination weights; and

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

9. The method of claim 8 wherein calculating each of the plurality of combination weights comprises:

calculating an autocorrelation metric corresponding to the autocorrelation sequence;

calculating a ratio of the usable portion of the PPG signal;

calculating a correlation coefficient for the autocorrelation sequence; and

calculating the combination weight based on the autocorrelation metric, the ratio, and the correlation coefficient.

10. The method of claim 8 further comprising:

calculating a plurality of sequence ages, wherein each sequence age corresponds to one of the plurality of autocorrelation sequences; and

calculating a combined autocorrelation age based on the plurality of combination weights and the plurality of sequence ages.

11. A patient monitoring system comprising:

a processor configured to;

generate a plurality of morphology metric signals based on a photoplethysmograph (PPG) signal;

generate a plurality of autocorrelation sequences, wherein each autocorrelation sequence is derived from a respective one of the morphology metric signals; and

determine physiological information from the plurality of autocorrelation sequences.

12. The patient monitoring system of claim 11, wherein the plurality of morphology metric signals comprise a down metric signal, a kurtosis metric signal, and a DSD metric signal.

13. The patient monitoring system of claim 11, the processor further configured to generate a plurality of autocorrelation metrics, wherein each autocorrelation metric corresponds to one of the autocorrelation sequences.

14. The patient monitoring system of claim 13, the processor further configured to:

identify a plurality of local minima points for each of the autocorrelation sequences;

normalize the local minima based on a peak for each of the autocorrelation sequences;

average the normalized local minimas for each of the autocorrelation sequences; and

generate the plurality of autocorrelation metrics based on the averages.

15. The patient monitoring system of claim 13, the processor further configured to:

generate an autocorrelation for each of the morphology metric signals;

calculate a ratio of the usable portion of each of the PPG signals;

calculate a plurality of filter weights based on the ratios and the autocorrelation metrics; and

filter the autocorrelations based on the filter weights to generate the autocorrelation sequences.

16. The patient monitoring system of claim 15, the processor further configured to calculate a sequence age for each of the autocorrelation sequences based on the filter weight for the autocorrelation sequence, a current age for the autocorrelation sequence, and a previous age for the autocorrelation sequence.

17. The patient monitoring system of claim 11, the processor further configured to combine the plurality of autocorrelation sequences to generate a combined autocorrelation sequence, and determine the physiological information based on the combined autocorrelation sequence.

18. The patient monitoring system of claim 17, the processor further configured to calculate a plurality of combination weights and generate the combined autocorrelation sequence based on the plurality of autocorrelation sequences and the plurality of combination weights.

19. The patient monitoring system of claim 18, the processor further configured to:

calculate an autocorrelation metric for each of the autocorrelation sequences;

calculate a ratio of the usable portion of the PPG signal for each of the autocorrelation sequences;

calculate a correlation coefficient for each of the autocorrelation sequences; and

calculate the combination weights based on the autocorrelation metrics, the ratios, and the correlation coefficients.

20. The patient monitoring system of claim 18, the processor further configured to calculate a plurality of sequence ages corresponding to the plurality of autocorrelation sequences and calculate a combined autocorrelation age based on the plurality of combination weights and the plurality of sequence ages.