RESPIRATION RATE DETERMINATION IN IMPEDANCE PNEUMOGRAPHY

• US 20150272474A1
• Filed: 03/25/2014
• Published: 10/01/2015
• Est. Priority Date: 03/25/2014
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Genc, Sahika
• Granted Patent: US 9,706,945 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0809 by impedance pneumography
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7271 Specific aspects of physiological measurement analysis A61B5/316, A61B5/349 and A61B5/374 take precedence

Abstract

A system for monitoring respiration, and a method for determining respiration rate, is disclosed. In one embodiment, the respiration rate is determined from a power spectral density template that is updated, or not, based on whether a power spectral density for a current window of in-band filtered impedance respiration signal is determined to be noisy or not.

Claims

1. A method for deriving a signal quality metric, comprising:

acquiring an impedance respiration signal;

generating a series of sets of windowed data from the impedance respiration signal, wherein each window of data comprises data points acquired over a specified duration and is offset from a preceding window by a fixed interval;

filtering the impedance respiration signal through at least an in-band filter to generate an in-band filtered signal;

for each window of in-band filtered signals, generating a respective power spectral density;

determining whether the respective power spectral density for a current window is noisy or not noisy;

if the respective power spectral density for the current window is not noisy, updating a previous power spectral density template based on the respective power spectral density for the current window to generate a current power spectral density template;

if the respective power spectral density for the current window is noisy, not updating the previous power spectral density template;

identifying at least one peak and the corresponding frequencies and powers associated with the identified peaks within the current power spectral density template; and

determining a respiration rate based on the at least one identified peak.

2. The method of claim 1, wherein the impedance respiration signal is acquired at 60 Hz or an integer multiple of 60 Hz.

3. The method of claim 1, wherein the specified duration is approximately 24 seconds.

4. The method of claim 1, wherein the specified duration comprises an interval in which two breaths are determined to occur within the specified duration.

5. The method of claim 1, wherein the fixed interval comprises 2 seconds.

6. The method of claim 1, wherein filtering the impedance respiration signal comprises filtering the impedance respiration signal though at least one infinite impulse response filter.

7. The method of claim 1, wherein determining whether the respective power spectral density for the current window is noisy or not noisy, comprises:

determining a correlation vector using a set of stored power spectral density templates; and

determining whether a statistical descriptor of the correlation vector exceeds a specified threshold;

wherein the respective power spectral density for the current window is determined to be noisy if the statistical descriptor of the correlation vector does not exceed the specified threshold; and

wherein the respective power spectral density for the current window is determined to not be noisy if the statistical descriptor of the correlation vector does exceed the specified threshold.

8. The method of claim 7, wherein the statistical descriptor is one of a mean, a median, a mode, a minimum, or a maximum.

9. The method of claim 1, wherein updating the previous power spectral density template based on the respective power spectral density for the current window comprises weighting the respective power spectral density for the current window greater than the previous power spectral density template.

10. The method of claim 1, wherein determining the respiration rate comprises:

determining a power-weighted average of the frequency components for a plurality of identified peaks; and

determining a respiration rate from the power weighted average.

11. The method of claim 1, wherein determining the respiration rate comprises:

determining a dominant peak within the current power spectral density template; and

determining a respiration rate based on the dominant peak.

12. A non-transitory, computer-readable medium configured to store one or more routines executable by a processing system, the routines, when executed, causing acts to be performed comprising:

applying an in-band filter to an impedance respiration signal to generate an in-band filtered signal;

generating a respective power spectral density for each of a series of windows of the in-band filtered signal, wherein each window comprises data points acquired over a specified duration and is offset from adjacent windows by a fixed interval;

processing the respective power spectral density for each window in sequence to determine whether the respective power spectral density for each window is noisy or not noisy;

updating, if the respective power spectral density for a current window is not noisy, a previous power spectral density template based on the respective power spectral density for the current window to generate a current power spectral density template; and

determining a respiration rate based on one or more peaks identified in the current power spectral density template.

13. The non-transitory, computer-readable medium of claim 12, wherein determining whether the respective power spectral density for each window is noisy or not noisy, comprises:

determining a correlation vector using a set of stored power spectral density templates; and

determining whether a statistical descriptor of the correlation vector exceeds a specified threshold;

wherein the power spectral density for the respective window is determined to be noisy if the statistical descriptor of the correlation vector does not exceed the specified threshold; and

wherein the power spectral density for the respective window is determined to not be noisy if the statistical descriptor of the correlation vector does exceed the specified threshold.

14. The non-transitory, computer-readable medium of claim 12, wherein determining the respiration rate comprises:

determining a power-weighted average of frequency components for a plurality of identified peaks; and

determining a respiration rate from the power weighted average.

15. The non-transitory, computer-readable medium of claim 12, wherein determining the respiration rate comprises:

determining a dominant peak within the current power spectral density template; and

determining a respiration rate based on the dominant peak.

16. A physiological monitoring system, comprising:

a memory storing one or more routines; and

a processing component configured to execute the one or more routines stored in the memory, wherein the one or more routines, when executed by the processing component, cause acts to be performed comprising;

acquiring an impedance respiration signal;

generating a series of data windows from the impedance respiration signal, wherein each data window comprises data points acquired over a specified duration and is offset from a preceding window by a fixed interval;

filtering the impedance respiration signal through at least an in-band filter to generate an in-band filtered signals;

generating a respective power spectral density for each window of in-band filtered signals;

determining whether the respective power spectral density for a current window is noisy or not noisy;

updating, if the respective power spectral density for the current window is not noisy, a previous power spectral density template based on the respective power spectral density for the current window to generate a current power spectral density template; and

determining a respiration rate based the current power spectral density template.

17. The physiological monitoring system of claim 16, wherein determining whether the respective power spectral density for each window is noisy or not noisy, comprises:

determining a correlation vector using a set of stored power spectral density templates; and

determining whether a statistical descriptor of the correlation vector exceeds a specified threshold;

wherein the respective power spectral density for the current window is determined to be noisy if the statistical descriptor of the correlation vector does not exceed the specified threshold; and

wherein the respective power spectral density for the current window is determined to not be noisy if the statistical descriptor of the correlation vector does exceed the specified threshold.

18. The physiological monitoring system of claim 16, wherein determining the respiration rate comprises:

determining a power-weighted average of frequency components for a plurality of identified peaks within the current power spectral density template; and

determining a respiration rate from the power weighted average.

19. The physiological monitoring system of claim 16, wherein determining the respiration rate comprises:

determining a dominant peak within the current power spectral density template; and

determining a respiration rate based on the dominant peak.

20. The physiological monitoring system of claim 16, wherein the physiological monitoring system comprises one of an impedance respiration monitor or an electrocardiography (ECG) monitor.