Non-invasive systems and methods for identifying respiratory disturbances experienced by a subject

Abstract

An example method for detecting respiratory disturbances experienced by a subject can include receiving an airflow signal and at least one of an acoustic or vibration signal, where the airflow, acoustic, and/or vibration signals are associated with the subject's breathing. At least one feature can be extracted from the airflow signal and at least one feature can be extracted from at least one of the acoustic or vibration signal. Based on the extracted features, at least one respiratory disturbance can be detected. The respiratory disturbance can be flow limited breath or inspiratory flow limitation (“IFL”).

1 . (canceled) 
     
     
         2 . A method, comprising:
 positioning an adjustable mandibular displacement device in an oral cavity of a subject during a test period;   receiving, using a computing device, at least one signal associated with the subject's breathing during the test period, wherein the at least one signal is an airflow signal, an acoustic signal, or a vibration signal;   extracting, using the computing device, a plurality of features from the at least one signal;   calculating, using the computing device, a correlation between the extracted features; and   detecting, using the computing device, at least one respiratory disturbance based on the correlation between the extracted features; and   adjusting a protrusion level of the adjustable mandibular displacement device during the test period.   
     
     
         3 . The method of  claim 2 , wherein adjusting a protrusion level of the adjustable mandibular displacement device during the test period further comprises titrating the protrusion level of the adjustable mandibular displacement device. 
     
     
         4 . The method of  claim 2 , wherein detecting, based on the correlation between the extracted features, at least one respiratory disturbance further comprises inputting the correlation between the extracted features into a machine learning module that is executed by the computing device, wherein an output value of the machine learning module indicates occurrence of the at least one respiratory disturbance. 
     
     
         5 . The method of  claim 2 , wherein the extracted features include a feature extracted from a portion of the airflow signal corresponding to at least a portion of an inspiration portion of a breath. 
     
     
         6 . The method of  claim 2 , wherein the extracted features include a feature extracted from the airflow signal in a time or frequency domain. 
     
     
         7 . The method of  claim 6 , wherein the extracted features include at least one of a shape, magnitude, distribution, duration, or energy of the airflow signal. 
     
     
         8 . The method of  claim 2 , wherein the at least one of the acoustic signal or the vibration signal is measured using a sensor mounted on the adjustable mandibular displacement device. 
     
     
         9 . The method of  claim 8 , wherein the sensor is a microphone, accelerometer, or strain gauge configured to measure the acoustic signal or an accelerometer or strain gauge configured to measure the vibration signal. 
     
     
         10 . The method of  claim 9 , wherein the microphone, accelerometer, or strain gauge configured to measure the acoustic signal is attached to a housing or dental tray of the adjustable mandibular displacement device. 
     
     
         11 . The method of  claim 9 , wherein the accelerometer or strain gauge configured to measure the vibration signal is attached to a housing, dental tray or bracket of the adjustable mandibular displacement device. 
     
     
         12 . The method of  claim 2 , wherein the adjustable mandibular displacement device reduces airflow through the subject's oral cavity. 
     
     
         13 . The method of  claim 2 , wherein the adjustable mandibular displacement device is firmly attached to the subject's teeth. 
     
     
         14 . The method of  claim 2 , wherein the at least one respiratory disturbance is flow limited breath or inspiratory flow limitation (IFL). 
     
     
         15 . The method of  claim 2 , further comprising diagnosing or assessing the subject with high upper airway resistance (HUAR). 
     
     
         16 . The method of  claim 2 , wherein the at least one respiratory disturbance is detected in real time while the subject is sleeping. 
     
     
         17 . The method of  claim 2 , wherein at least two signals associated with the subject's breathing are received using the computing device, and wherein the extracted features are extracted from the airflow signal and at least one of the acoustic signal or the vibration signal. 
     
     
         18 . The method of  claim 17 , wherein the extracted features are extracted from the airflow signal, the acoustic signal, and the vibration signal. 
     
     
         19 . A system, comprising:
 an adjustable mandibular displacement device;   a sensor for measuring at least one of an airflow signal, an acoustic signal, or a vibration signal associated with the subject's breathing;   a processor; and   a memory operatively coupled to the processor, the memory having computer-executable instructions stored thereon that, when executed by the processor, cause the processor to:
 receive the at least one signal associated with the subject's breathing; 
 extract a plurality of features from the at least one signal; 
   calculate a correlation between the extracted features;
 detect, based on the correlation between the extracted features, at least one respiratory disturbance; and 
 adjust a protrusion level of the adjustable mandibular displacement device. 
   
     
     
         20 . The system of  claim 19 , wherein adjusting a protrusion level of the adjustable mandibular displacement device further comprises titrating the protrusion level of the adjustable mandibular displacement device. 
     
     
         21 . The system of  claim 19 , wherein the sensor is arranged in proximity to the subject's oral or nasal cavity. 
     
     
         22 . The system of  claim 19 , wherein the sensor is arranged in the subject's oral cavity. 
     
     
         23 . The system of  claim 19 , wherein the sensor is a microphone, accelerometer, or strain gauge configured to measure the acoustic signal. 
     
     
         24 . The system of  claim 23 , wherein the microphone, accelerometer, or strain gauge is attached to a housing or dental tray of the mandibular displacement device. 
     
     
         25 . The system of  claim 19 , wherein the sensor is an accelerometer or strain gauge configured to measure the vibration signal. 
     
     
         26 . The system of  claim 19 , wherein the sensor is attached to a housing, dental tray or bracket of the adjustable mandibular displacement device. 
     
     
         27 . The system of  claim 19 , wherein the adjustable mandibular displacement device reduces airflow through the subject's oral cavity. 
     
     
         28 . The system of  claim 19 , wherein the adjustable mandibular displacement device is firmly attached to the subject's teeth. 
     
     
         29 . The system of  claim 19 , wherein at least two signals associated with the subject's breathing are received using the computing device, and wherein the extracted features are extracted from the airflow signal and at least one of the acoustic signal or the vibration signal. 
     
     
         30 . The system of  claim 29 , wherein the extracted features are extracted from the airflow signal, the acoustic signal, and the vibration signal. 
     
     
         31 . The system of  claim 19 , wherein the memory has further computer-executable instructions stored thereon that, when executed by the processor, cause the processor to diagnose or assess the subject with high upper airway resistance (HUAR).