Multistage method and system for estimating respiration parameters from acoustic signal

• US 20120253214A1
• Filed: 03/30/2011
• Published: 10/04/2012
• Est. Priority Date: 03/30/2011
• Status: Active Grant

• Current Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Original Assignee: Sharp Kabushiki Kaisha (Hon Hai Precision Industry Co., Ltd.)
• Inventors: Fu, Yongji, Lai, Yungkai Kyle, Hallberg, Bryan Severt
• Granted Patent: US 8,663,124 B2
• Time in Patent Office: Days
• US Class Current: 600/529
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7289 Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise

Abstract

A multistage system and method for estimating respiration parameters from an acoustic signal. At a first stage, the method and system detect and isolate portions of the signal that exhibit long-term, moderate amplitude noise by analyzing cumulative energies in the signal, and portions of the signal that exhibit short-term, high amplitude noise by analyzing peak energies in the signal. At a second stage, the method and system filter heart sound from the signal energy envelope by applying an adaptive filter that minimizes the loss of respiration sound. At a third stage, the system and method isolate respiration phases in the signal by identifying trends in the energy envelope. Once respiration phases are isolated, these phases are used to estimate respiration parameters, such as respiration rate and I/E ratio.

Claims

1. A method for processing an acoustic signal, comprising the steps of:

acquiring by a respiration monitoring system an acoustic signal recording body sounds;

isolating by the system noisy portions of the signal based at least in part on cumulative energies and peak energies in the signal;

detecting by the system an energy envelope for non-noisy portions of the signal;

filtering by the system the energy envelope using an adaptive filter;

isolating by the system respiration phases in the energy envelope at least in part by identifying trends in the energy envelope;

estimating by the system a respiration parameter based at least in part on the respiration phases; and

outputting by the system information based at least in part on the respiration parameter estimate.

2. The method of claim 1, wherein the first isolating step comprises the substeps of detecting first noisy portions of the signal based at least in part on cumulative energies in the signal;

detecting second noisy portions of the signal based at least part on peak energies in the signal; and

identifying the isolated noisy portions based at least in part on the first noisy portions and the second noisy portions.

3. The method of claim 2, wherein the identifying substep comprises designating, as third noisy portions, intersections between first noisy portions that envelop second noisy portions and second noisy portions that are enveloped by first noisy portions.

4. The method of claim 2, wherein the identifying substep comprises designating, as third noisy portions, unions of first noisy portions that do not envelop second noisy portions and second noisy portions that are not enveloped by first noisy portions.

5. The method of claim 1, wherein the filtering step comprises the substeps of identifying unwanted peaks in the energy envelope that are attributable to heart sound;

decreasing a high cutoff frequency in response to identifying the unwanted peaks; and

applying the decreased high cutoff frequency to the energy envelope.

6. The method of claim 5, wherein the filtering step further comprises the substeps of identifying wanted peaks that were removed from the energy envelope in response to applying the decreased high cutoff frequency;

and increasing the high cutoff frequency in response to identifying the wanted peaks.

7. The method of claim 1, wherein the second isolating step comprises the substeps of identifying candidate peaks at maxima of the energy envelope;

identifying candidate valleys at minima of the energy envelope;

selecting significant peaks from among the candidate peaks using heights of the candidate peaks;

selecting significant valleys from among the candidate valleys using heights of the candidate valleys;

detecting silent phases in the energy envelope based at least in part on rise rates from the significant valleys; and

isolating the respiration phases based at least in part on the significant valleys and the silent phases.

8. The method of claim 7, wherein the isolating substep comprises identifying a true silent phase among the silent phases based at least in part on a respiration phase sequence exhibited by the energy envelope.

9. The method of claim 7, wherein the isolating substep comprises identifying a silent expiration phase among the silent phases based at least in part on a respiration phase sequence exhibited by the energy envelope.

10. The method of claim 1 wherein the respiratory monitoring system is a portable ambulatory monitoring device.

11. A respiration monitoring system, comprising:

a sound capture system adapted to acquire an acoustic signal recording body sounds;

an acoustic signal processing system adapted to receive from the sound capture system the signal, isolate noisy portions of the signal based at least in part on cumulative energies and peak energies in the signal, detect an energy envelope for non-noisy portions of the signal, filter the energy envelope using an adaptive filter, isolate respiration phases in the energy envelope at least in part by identifying trends in the energy envelope and estimate a respiration parameter based at least in part on the respiration phases; and

a respiration data output system adapted to output information based at least in part on the respiration parameter estimate.

12. The monitoring system of claim 11, wherein the processing system is adapted to detect first noisy portions of the signal based at least in part on cumulative energies in the signal, is adapted to detect second noisy portions of the signal based at least part on peak energies in the signal, and is adapted to identify the isolated noisy portions based at least in part on the first noisy portions and the second noisy portions.

13. The monitoring system of claim 12, wherein the processing system is adapted to designate, as third noisy portions, intersections between first noisy portions that envelop second noisy portions and second noisy portions that are enveloped by first noisy portions.

14. The monitoring system of claim 12, wherein the processing system is adapted to designate, as third noisy portions, unions of first noisy portions that do not envelop second noisy portions and second noisy portions that are not enveloped by first noisy portions.

15. The monitoring system of claim 11, wherein the processing system is adapted to identify unwanted peaks in the energy envelope that are attributable to heart sound, is adapted to decrease a high cutoff frequency in response to identifying the unwanted peaks, and is adapted to apply the decreased high cutoff frequency to the energy envelope.

16. The monitoring system of claim 15, wherein the processing system is further adapted to identify wanted peaks that were removed from the energy envelope in response to applying the decreased high cutoff frequency, and is adapted to increase the high cutoff frequency in response to identifying the wanted peaks.

17. The monitoring system of claim 11, wherein the processing system is adapted to identify candidate peaks at maxima of the energy envelope, identify candidate valleys at minima of the energy envelope, select significant peaks from among the candidate peaks using heights of the candidate peaks, select significant valleys from among the candidate valleys using heights of the candidate valleys, detect silent phases in the energy envelope based at least in part on rise rates from the significant valleys, and isolate the respiration phases based at least in part on the significant valleys and the silent phases.

18. The monitoring system of claim 17, wherein the processing system is adapted to identify a true silent phase among the silent phases based at least in part on a respiration phase sequence exhibited by the energy envelope.

19. The monitoring system of claim 17, wherein the processing system is adapted to identify a silent expiration phase among the silent phases based at least in part on a respiration phase sequence exhibited by the energy envelope.

20. The respiratory monitoring system of claim 11, wherein the monitoring system is a portable ambulatory monitoring device.