Methods and apparatus for performing dynamic respiratory classification and tracking

• US 10,426,426 B2
• Filed: 07/04/2017
• Issued: 10/01/2019
• 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
• Primary Examiner(s): Mallari, Patricia
• Assistant Examiner(s): Portillo, Jairo H
• Application Number:
  US15/641,262
• Publication Number:
  US 20180021010A1
• Time in Patent Office: 819 Days
• Field of Search: None
• 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 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0823 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 5/6823 Trunk, e.g., chest, back, abdomen, hip
• 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
• CPC Class Codes: G16H 40/63 for local operation

Abstract

A method for detecting thresholds in a breathing session is disclosed. The method comprises recording breathing sounds of a subject using a microphone. The method further comprises processing the breathing sounds to generate an audio respiratory signal and recognizing a plurality of breath cycles from the audio respiratory signal. Additionally, the method comprises extracting metrics related to a breath intensity and a breath rate from the plurality of breath cycles and producing a plurality of vectors using the metrics related to the breath intensity and the breath rate. Further, the method comprises calculating a master vector by summing the plurality of vectors and assigning each value in the master vector with a weighting coefficient and determining the thresholds using peak values in said master vector.

Claims

1. A method for detecting thresholds in a breathing session, said method comprising:

recording breathing sounds of a subject using a microphone;

processing said breathing sounds to generate an audio respiratory signal;

recognizing a plurality of breath cycles from said audio respiratory signal;

extracting metrics related to a breath intensity and a breath rate from said plurality of breath cycles;

producing a plurality of vectors using said metrics related to said breath intensity and said breath rate;

calculating a master vector by summing said plurality of vectors and assigning each value in said master vector with a weighting coefficient;

determining said thresholds using peak values in said master vector; and

wherein said thresholds consist of one of a ventilatory threshold or aerobic threshold and one of a respiratory compensation threshold, anaerobic threshold, and lactate threshold.

2. The method of claim 1, wherein said extracting further comprises:

extracting metrics related to a heart rate of said subject using a heart monitor.

3. The method of claim 2, wherein said producing said plurality of vectors further comprises using said metrics related to said heart rate of said subject.

4. The method of claim 1, wherein said producing said plurality of vectors comprises:

creating a corresponding base vector for each of said breath rate and said breath intensity using said metrics related to said breath rate and said breath intensity.

5. The method of claim 4, wherein said producing said plurality of vectors further comprises:

calculating first and second derivatives for each of said breath intensity and said breath rate using said corresponding base vector and producing a vector for each of said first and said second derivatives.

6. The method of claim 5, wherein said producing said plurality of vectors further comprises:

creating a time-shifted vector corresponding to a base vector for each of said breath intensity and said breath rate.

7. The method of claim 6, wherein said producing said plurality of vectors further comprises:

creating a first Boolean vector for each of said base vector, said first derivative vector, said second derivative vector and said time-shifted vector for each of said breath intensity and said breath rate, wherein said Boolean vector tracks minimum values in an associated vector.

8. The method of claim 7, wherein said producing said plurality of vectors further comprises:

creating a second Boolean vector for each of said base vector, said first derivative vector, said second derivative vector and said time-shifted vector for each of said breath intensity and said breath rate, wherein said Boolean vector tracks maximum values in an associated vector.

9. The method of claim 8, wherein said master vector tracks minimum values and maximum values from each of said first Boolean vector and said second Boolean vector for each of said breath intensity and said breath rate.

10. The method of claim 9, wherein highest values in said master vector indicate the likeliest candidates for said thresholds.

11. The method of claim 1, further comprising trimming said master vector to eliminate out of bound candidates prior to determining said thresholds.

12. A non-transitory 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 detecting thresholds in a breathing session, said method comprising:

recording breathing sounds of a subject using a microphone;

processing said breathing sounds to generate an audio respiratory signal;

recognizing a plurality of breath cycles from said audio respiratory signal;

extracting metrics related to a breath intensity and a breath rate from said plurality of breath cycles;

producing a plurality of vectors using said metrics related to said breath intensity and said breath rate;

calculating a master vector by summing said plurality of vectors and assigning each value in said master vector with a weighting coefficient;

determining said thresholds using peak values in said master vector; and

wherein said thresholds consist of one of a ventilatory threshold or aerobic threshold and one of a respiratory compensation threshold, anaerobic threshold, or lactate threshold.

13. The non-transitory computer-readable storage medium of claim 12, wherein said extracting further comprises:

extracting metrics related to a heart rate of said subject using a heart monitor.

14. The non-transitory computer-readable storage medium of claim 13, wherein said producing said plurality of vectors further comprises using said metrics related to said heart rate of said subject.

15. The non-transitory computer-readable storage medium of claim 13, wherein said producing said plurality of vectors comprises:

creating a corresponding base vector for each of said breath rate and said breath intensity using said metrics related to said breath rate and said breath intensity.

16. The non-transitory computer-readable storage medium of claim 15, wherein said producing said plurality of vectors further comprises:

calculating first and second derivatives for each of said breath intensity and said breath rate using said corresponding base vector and producing a vector for each of said first and said second derivatives.

17. The non-transitory computer-readable storage medium of claim 16, wherein said producing said plurality of vectors further comprises:

creating a time-shifted vector corresponding to a base vector for each of said breath intensity and said breath rate.

18. The non-transitory computer-readable storage medium of claim 17, wherein said producing said plurality of vectors further comprises:

creating a first Boolean vector for each of said base vector, said first derivative vector, said second derivative vector and said time-shifted vector for each of said breath intensity and said breath rate, wherein said Boolean vector tracks minimum values in an associated vector.

19. The non-transitory computer-readable storage medium of claim 18, wherein said producing said plurality of vectors further comprises:

creating a second Boolean vector for each of said base vector, said first derivative vector, said second derivative vector and said time-shifted vector for each of said breath intensity and said breath rate, wherein said Boolean vector tracks maximum values in an associated vector.

20. The non-transitory computer-readable storage medium of claim 19, wherein said master vector tracks minimum values and maximum values from each of said first Boolean vector and said second Boolean vector for each of said breath intensity and said breath rate.

21. The non-transitory computer-readable storage medium of claim 20, wherein highest values in said master vector indicate the likeliest candidates for said thresholds.

22. An apparatus for detecting thresholds in a breathing session, said apparatus comprising:

a microphone for capturing breathing sounds of a subject;

a memory comprising an application for determining ventilatory thresholds from 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;

record breathing sounds of a subject using a microphone;

process said breathing sounds to generate an audio respiratory signal;

recognize a plurality of breath cycles from said audio respiratory signal;

extract metrics related to a breath intensity and a breath rate from said plurality of breath cycles;

produce a plurality of vectors using said metrics related to said breath intensity and said breath rate;

calculate a master vector by summing said plurality of vectors and assigning each value in said master vector with a weighting coefficient;

determine said thresholds using peak values in said master vector; and

wherein said thresholds consist of one of ventilatory threshold or aerobic threshold, and one of a respiratory compensation threshold, anaerobic threshold, and lactate threshold.