# US 2014/0155773 A1 · Methods and apparatus for performing dynamic respiratory classification and tracking > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0155773 A1 - Document ID: US20140155773A1 - Status: Published application - Kind: A1 - Application number: 13/920,655 - Filed: 2013-06-18 - Published: 2014-06-05 - Earliest claimed: 2012-06-18 - Assignees: Breathresearch - Applicants: BreathResearch - Inventors: Charalampos Christos Stamatopoulos; Panagiotis Giotis; Nirinjan Bikko Yee - Web page: https://patentgrep.com/patent/US20140155773A1 ## CPC - A61B 7/003: {Detecting lung or respiration noise} - A61B 7/04: Electric stethoscopes - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/0823: {Detecting or evaluating cough events} - A61B 5/0826: {Detecting or evaluating apnoea events (sleep apnoea A61B5/4818)} - A61B 5/6819: {Nose} - A61B 5/6831: {Straps, bands or harnesses} - A61B 5/725: {using specific filters therefor, e.g. Kalman or adaptive filters (specific diagnostics methods using using bioelectric or biomagnetic signals A61B5/316)} - A61B 5/7278: {Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals} - A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)} - 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)} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## 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 ### Claim 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. ### Claim 2 (depends on 1) 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. ### Claim 3 (depends on 1) 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. ### Claim 4 (depends on 3) 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. ### Claim 5 (depends on 3) The method of claim 3, wherein each class within said plurality of classes is selected from a group consisting of: inhalations; exhalations; rest periods. ### Claim 6 (depends on 3) 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. ### Claim 7 (depends on 6) 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. ### Claim 8 (depends on 7) 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. ### Claim 9 (depends on 1) 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. ### Claim 10 (depends on 1) 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. ### Claim 11 (depends on 1) 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. ### Claim 12 (depends on 1) 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. ### Claim 13 (depends on 11) 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. ### Claim 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. ### Claim 15 (depends on 14) 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. ### Claim 16 (depends on 15) 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. ### Claim 17 (depends on 15) 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. ### Claim 18 (depends on 17) 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. ### Claim 19 (depends on 14) 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. ### Claim 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.