Systems and methods for ridge selection in scalograms of signals

• US 8,295,567 B2
• Filed: 10/03/2008
• Issued: 10/23/2012
• Est. Priority Date: 06/30/2008
• Status: Expired due to Fees

• Current Assignee: Nellcor Puritan Bennett Ireland
• Original Assignee: Nellcor Puritan Bennett Ireland
• Inventors: Watson, James, Addison, Paul Stanley, Clifton, David
• Primary Examiner(s): Pham, Long
• Application Number:
  US12/245,326
• Publication Number:
  US 20090324034A1
• Time in Patent Office: 1,481 Days
• Field of Search: 382/128, 382/207, 381/94.1, 600/323, 607/7
• US Class Current: 382/128
• CPC Class Codes: A61B 5/0806 by whole-body plethysmography measuring blood flow using plethysmography A61B5/0295; impedance plethysmography A61B5/053; measuring volume of the body or parts thereof A61B5/1073
• 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 5/726 using Wavelet transforms
• CPC Class Codes: G06F 2218/10 by analysing the shape of a waveform, e.g. extracting parameters relating to peaks

Abstract

According to embodiments, systems, devices, and methods for ridge selection in scalograms are disclosed. Ridges or ridge components are features within a scalogram which may be computed from a signal such as a physiological (e.g., photoplethysmographic) signal. Ridges may be identified from one or more scalograms of the signal. Parameters characterizing these ridges may be determined. Based at least in part on these parameters, a ridge density distribution function is determined. A ridge is selected from analyzing this ridge density distribution function. In some embodiments, the selected ridge is used to determine a physiological parameter such as respiration rate.

Claims

1. A method comprising:

transforming a signal based at least in part on a wavelet transform to generate a transformed signal,

generating a first wavelet scalogram based at least in part on the transformed signal,

detecting ridges within a region of the first wavelet scalogram,

determining one or more parameters for the detected ridges,

determining a ridge density distribution function based at least in part on the one or more parameters, and

selecting a ridge based at least in part on the ridge having a scale corresponding to a maximum value of the ridge density distribution function.

2. The method of claim 1, wherein the signal is a photoplethysmographic signal.

3. The method of claim 1, further comprising filtering the first wavelet scalogram.

4. The method of claim 1, further comprising generating a second wavelet scalogram based at least in part on a signal derived from the first wavelet scalogram and detecting ridges within a region of the second wavelet scalogram.

5. The method of claim 1, wherein the parameters of the detected ridges comprise ridge power, ridge energy, ridge energy density, ridge amplitude variability, ridge scale variability, ridge consistency, intrinsic scale, ridge length, maximum ridge amplitude, standard deviation of intrinsic scale, standard deviation of amplitude, mean scale, median scale, mean amplitude, median amplitude, and/or strength-length product.

6. The method of claim 1, further comprising determining a physiological parameter based at least in part on the selected ridge.

7. The method of claim 6, wherein the physiological parameter comprises respiration rate.

14. The method of claim 1, wherein the ridge density distribution function is calculated as a histogram of the one or more parameters for a given scale.

15. The method of claim 1, wherein the ridge density distribution is at least partially based on a set of weighting coefficients for each ridge.

8. A computer-readable medium comprising computer-executable instructions which, if executed by a processor, cause the processor to carry out a method for determining a physiological parameter from one or more scalograms of a signal comprising:

transforming an input signal at least in part on using a wavelet transform to generate a transformed signal,

generating a first wavelet scalogram based at least in part on the transformed signal,

detecting ridges within a region of the first wavelet scalogram,

determining one or more parameters for the detected ridges,

determining a ridge density distribution function based at least in part on the one or more parameters, and

selecting a ridge based at least in part on the ridge having a scale corresponding to a maximum value of the ridge density distribution function.

9. The computer-readable medium of claim 8, wherein the signal is a photoplethysmographic signal.

10. The computer-readable medium of claim 8, further comprising computer-executable instructions which, if executed by a processor, cause the processor to generate a second wavelet scalogram based at least in part on a signal derived from the first wavelet scalogram and detect ridges within a region of the second wavelet scalogram.

11. The computer-readable medium of claim 8, wherein the parameters of the detected ridges comprise ridge power, ridge energy, ridge energy density, ridge amplitude variability, ridge scale variability, ridge consistency, intrinsic scale, ridge length, maximum ridge amplitude, standard deviation of intrinsic scale, standard deviation of amplitude, mean scale, median scale, mean amplitude, median amplitude, and/or strength-length product.

12. The computer-readable medium of claim 8, further comprising computer-executable instructions which, if executed by a processor, cause the processor to determine a physiological parameter based on the selected ridge.

13. The computer-readable medium of claim 12, wherein the physiological parameter comprises respiration rate.

16. The method of claim 8, wherein the ridge density distribution function is calculated as a histogram of the one or more parameters for a given scale.

17. The method of claim 8, wherein the ridge density distribution is at least partially based on a set of weighting coefficients for each ridge.