METHODS AND APPARATUS FOR PERFORMING DYNAMIC RESPIRATORY CLASSIFICATION AND TRACKING

• US 20140155773A1
• Filed: 06/18/2013
• Published: 06/05/2014
• Est. Priority Date: 06/18/2012
• Status: Active Grant

• Current Assignee: Vuaant Incorporated
• Original Assignee: Breathresearch, Inc.
• Inventors: Stamatopoulos, Charalampos Christos, Giotis, Panagiotis, Yee, Nirinjan Bikko
• Granted Patent: US 9,814,438 B2
• Time in Patent Office: Days
• US Class Current: 600/529
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0823 Detecting or evaluating cough events
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/6819 Nose
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7278 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
• CPC Class Codes: A61B 5/7465 Arrangements for interactive communication between patient and care services, e.g. by using a telephone network telemetry of measured physiological signal A61B5/0002
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: A61B 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R

Abstract

A method for performing dynamic classification of a breathing session is disclosed. The method comprises capturing breathing sounds of a subject using a microphone. Further, it comprises recognizing a plurality of breath cycles and a plurality of breath phases within each of the plurality of breath cycles from the breathing sounds. It also comprises detecting characteristics regarding the plurality of breath cycles and the plurality of breath phases. Finally, it comprises extracting metrics concerning a breath pattern quality of the subject using the detected characteristics.

Claims

1. A method for performing acoustic dynamic classification of a breathing session, said method comprising:

capturing breathing sounds of a subject using a microphone;

processing said breathing sounds to generate an audio respiratory signal;

recognizing a plurality of breath cycles and a plurality of breath phases within each of said plurality of breath cycles from said audio respiratory signal;

detecting characteristics regarding said plurality of breath cycles and said plurality of breath phases; and

extracting metrics concerning a breath pattern quality of said subject using said characteristics.

2. The method of claim 1, wherein each breath phase within said plurality of breath phases is selected from a group consisting of:

inhale;

transition;

exhale and rest.

3. The method of claim 1, wherein said recognizing comprises:

obtaining a first envelope shaped spectral pattern from said audio respiratory signal;

classifying lobes of said first envelope shaped spectral pattern into a plurality of classes; and

defining said plurality of breath cycles and said plurality of breath phases using timestamps obtained from said classifying.

4. The method of claim 3, wherein said obtaining comprises:

calculating a spectral centroid of each block of said audio respiratory signal; and

filtering said audio respiratory signal with a low pass filter tuned to a minimum value of said spectral centroid.

5. The method of claim 3, wherein each class within said plurality of classes is selected from a group consisting of:

inhalations;

exhalations;

rest periods.

6. The method of claim 3, wherein said classifying comprises:

calculating a second envelope shaped spectral pattern using a window, wherein said window has a size set according to a respiratory rate;

normalizing said envelope shaped spectral pattern;

detecting peaks of said envelope shaped spectral pattern;

determining a start timestamp and an end timestamp of each breath cycle using said peaks; and

calculating a low threshold and a high threshold by using a moving average filter on said second envelope shaped spectral pattern, wherein said low threshold detects signal presence, and further wherein said high threshold discriminates between inhalation and exhalation events.

7. The method of claim 6, wherein calculating said respiratory rate comprises:

calculating an auto-correlation function (ACF) of said first envelope shaped spectral pattern; and

determining a peak of said ACF; and

using said peak to determine said estimate respiratory rate.

8. The method of claim 7, wherein said respiratory rate is used to determine a ventilatory threshold, wherein said ventilatory threshold occurs when a rate of change of said respiratory rate is greatest.

9. The method of claim 1, wherein said characteristics are selected from a group consisting of:

inhale duration, pause duration, exhale duration, rest duration, a wheeze source, a wheeze type, a cough type, a cough source, choppiness, smoothness, attack and decay.

10. The method of claim 1, wherein said metrics are selected from a group consisting of:

respiratory rate;

depth;

tension;

nasal wheeze;

tracheal wheeze;

pre-apnea;

apnea;

ramp;

flow;

variability; and

inhale/exhale ratio.

11. The method of claim 1, wherein at least one of said characteristics is wheezing, and wherein said detecting comprises:

analyzing a block of said audio respiratory signal;

calculating a auto-correlation function (ACF) for said block;

calculate a linear predictive coefficient (LPC) for said block;

generating an inverse LPC filter along with a magnitude response for each block; and

analyzing said ACF and said magnitude response to identify said wheezing.

12. The method of claim 1, wherein at least one characteristic is coughing, and wherein said detecting comprises:

extracting a first plurality of descriptors that define a cough pattern from said audio respiratory signal;

comparing said first plurality of descriptors to a database comprising a second plurality of descriptors extracted from sample coughs; and

mapping an input cough from said audio respiratory signal to a closest match from said database.

13. The method of claim 11, wherein said descriptors are selected from a group consisting of:

attack time;

decay time;

envelope intensity;

spectral centroid;

spectral spread;

spectral kyrtosis; and

harmonicity.

14. A computer-readable storage medium having stored thereon, computer executable instructions that, if executed by a computer system cause the computer system to perform a method for performing acoustic dynamic classification of a breathing session, said method comprising:

capturing breathing sounds of a subject using a microphone;

processing said breathing sounds to generate an audio respiratory signal;

recognizing a plurality of breath cycles and a plurality of breath phases within each of said plurality of breath cycles from said audio respiratory signal;

detecting characteristics regarding said plurality of breath cycles and said plurality of breath phases; and

extracting metrics concerning a breath pattern quality of said subject using said characteristics.

15. The computer-readable medium as described in claim 14, wherein said recognizing comprises:

obtaining a first envelope shaped spectral pattern from said audio respiratory signal;

classifying lobes of said first envelope shaped spectral pattern into a plurality of classes; and

defining said plurality of breath cycles and said plurality of breath phases using timestamps obtained from said classifying.

16. The computer-readable medium as described in claim 15, wherein said obtaining comprises:

calculating a spectral centroid of each block of said audio respiratory signal; and

filtering said audio respiratory signal with a low pass filter tuned to a minimum value of said spectral centroid.

17. The computer-readable medium as described in claim 15, wherein said classifying comprises:

calculating a second envelope shaped spectral pattern using a window, wherein said window has a size set according to a respiratory rate;

normalizing said envelope shaped spectral pattern;

detecting peaks of said envelope shaped spectral pattern;

determining a start timestamp and an end timestamp of each breath cycle using said peaks; and

calculating a low threshold and a high threshold by using a moving average filter on said second envelope shaped spectral pattern, wherein said low threshold detects signal presence, and further wherein said high threshold discriminates between inhalation and exhalation events.

18. The computer-readable medium as described in claim 17, wherein calculating said respiratory rate comprises:

calculating an auto-correlation function (ACF) of said first envelope shaped spectral pattern; and

determining a peak of said ACF; and

using said peak to determine said estimate respiratory rate.

19. The computer-readable medium as described in claim 14, wherein at least one of said characteristics is wheezing, and wherein said detecting comprises:

analyzing a block of said audio respiratory signal;

calculating a auto-correlation function (ACF) for said block;

calculating a linear predictive coefficient (LPC) for said block;

generating an inverse LPC filter along with a magnitude response for each block; and

analyzing said ACF and said magnitude response to identify said wheezing.

20. An apparatus for performing acoustic dynamic classification of a breathing session, said apparatus comprising:

a microphone for capturing breathing sounds of a subject;

a memory comprises an application for performing dynamic classification of a breathing session stored therein; and

a processor coupled to said memory and said microphone, the processor being configured to operate in accordance with said application to;

process said breathing sounds to generate an audio respiratory signal;

recognize a plurality of breath cycles and a plurality of breath phases within each of said plurality of breath cycles from said audio respiratory signal;

detect characteristics regarding said plurality of breath cycles and said plurality of breath phases; and

extract metrics concerning a breath pattern quality of said subject using said characteristics.