Abstract: Various implementations of the present disclosure are directed to systems and methods for leak detection in a respiratory therapy system using acoustic data. According to some implementations, a method includes receiving acoustic data associated with airflow caused by operation of a respiratory therapy system during a sleep session of a user. The method also includes analyzing at least a portion of the acoustic data to determine a value of a parameter associated with the at least a portion of the acoustic data. The method further includes determining an occurrence of a leak during the sleep session in response to the determined value of the parameter satisfying a condition.

Abstract: A method of detecting rainout in a respiratory therapy system that includes a conduit fluidly coupled to a user interface comprises generating, via at least one microphone, acoustic data representative of noise associated with the respiratory therapy system. The method further comprises analyzing the acoustic data to detect a presence of liquid in the respiratory therapy system. The method further comprises causing an action to be performed, based on the detected presence of the liquid.

Abstract: Systems and methods are disclosed for categorizing and/or characterizing a user interface. The systems and methods include generating acoustic data associated with an acoustic reflection of an acoustic signal, the acoustic reflection being indicative of, at least in part, one or more features of a user interface coupled to a respiratory therapy device via a conduit. The systems and methods further include analyzing the generated acoustic data. The systems and methods further include categorizing and/or characterizing the user interface based, at least in part, on the analyzed acoustic data.

Abstract: A method includes generating acoustic data representative of at least one or more reflections of an acoustic signal. The one or more reflections are indicative of a length and/or a diameter of a conduit coupled to a respiratory therapy device. The method further includes analyzing the acoustic data to determine the length and/or diameter of the conduit. In some cases, analyzing the acoustic data includes determining a resonant frequency of the conduit, and determining the length of the conduit based at least in part on the resonant frequency. In some cases, analyzing the acoustic data includes comparing the acoustic data to predetermined sets of acoustic data that each correspond to a known length and/or diameter of the conduit, and selecting one of the predetermined sets of acoustic data that best matches the generated acoustic data. The selected set of acoustic data corresponds to the length and/or diameter of the conduit.

Abstract: A method includes generating, using one or more sensors, data. The data includes (i) environmental data related to an environment of a user and (ii) physiological data associated with the user during a sleep session. Based at least in part on the physiological data, a comfort score associated with the user during the sleep session is determined. The comfort score is indicative of a comfort level of the user during at least a portion of the sleep session. Based at least in part on the determined comfort score, a setting of one or more devices associated with the environment of the user is adjusted.

Abstract: Airflow parameters (e.g., flow rate and airflow pressure) of airflow generated by a flow generator of a respiratory therapy system can be measured during use and processed to automatically identify user interface and/or conduit identification information. This user interface and/or conduit identification information can be used to adjust settings of the respiratory therapy device, generate notifications (e.g., notifications of a detected change in user interface without concomitant, expected adjustment of settings of the respiratory therapy device), or otherwise facilitate respiratory therapy of the user or of other users. User interface and/or conduit identification information can be indicative of specific characteristics of the user interface and/or conduit (e.g., resonant frequencies, impedance, and the like), a style of the user interface (e.g., a face mask, nasal mask, or nasal pillow) and/or style of conduit, a specific manufacturer, a specific model, or other such identifiable information.

Abstract: A method includes data associated with a sleep session of a user, including respiration data associated with the user during at least a portion of the sleep session and audio data reproducible as one or more sounds during at least a portion of the sleep session. The method also includes determining a respiration signal associated with the user during the sleep session based, at least in part, on at least a portion of the data. The method also includes identifying an event experienced by the user during the sleep session based, at least in part on, at least a portion of the data. The method also includes causing to be communicated to the user via a user device a graphical representation of a portion of the respiration signal and an event indication that aids in identifying the identified event within the graphical representation of the portion of the respiration signal.

Abstract: A method for monitoring a sleep session of an individual comprises receiving data associated with a current sleep session of the individual; analyzing at least a portion of the received data to identify one or more sleep stages experienced by the individual during the current sleep session, the one or more sleep stages including a light sleep stage, a deep sleep stage, a typical rapid eye movement (REM) stage, an atypical REM stage, a wake stage, or any combination thereof; and generating a summary of the current sleep session, the summary including (i) a number of atypical REM sleep stages experienced by the individual during the sleep current sleep session, (ii) a time spent in atypical REM sleep stages during the current sleep session, or (iii) both (i) and (ii).

Abstract: Methods and apparatus provide monitoring of coughing and/or a sleep disordered breathing state of a person. One or more sensors may be configured for non-contact active and/or passive sensing. The processor(s) may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and/or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of coughing and/or sleep disordered breathing.

Abstract: The present disclosure relates to a method for determining a mouth leak status associated with a user of a respiratory device is disclosed. Airflow data associated with the user of the respiratory device is received. The respiratory device is configured to supply pressurized air to an airway of the user during a therapy session. The airflow data includes pressure data. The airflow data associated with the user is analyzed. Based at least in part on the analysis, the mouth leak status associated with the user is determined. The mouth leak status is indicative of whether or not air is leaking from a mouth of the user.

Abstract: Methods and apparatus provide monitoring of a sleep disordered breathing state of a person such as for screening. One or more sensors may be configured for non-contact active and/or passive sensing. The processor(s) (7304, 7006) may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and/or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of sleep disordered breathing. The processor may generate a sleep disordered breathing indicator based on the measure(s) of sleep disordered breathing.