Screening, diagnosis and monitoring of respiratory disorders

Abstract

A system screens, diagnoses, or monitors sleep disordered breathing of a patient. The system may include a nasal cannula, a conduit connected to the nasal cannula at a first end, an adaptor configured to receive a second end of the conduit and/or a portable computing device. The adaptor may be configured to position the second end of the conduit in proximity with a microphone of the portable computing device. Optionally, a processor may generate an indicator to guide placement of the adaptor for use. Such positioning may, in use, permit the microphone to generate a patient breathing sound signal via the adaptor for processor(s) of the device. The processor(s) may then process the breathing sound signal. The process may include detecting SDB events from an extracted and/or de-rectified loudness signal. The process may include computing a metric of severity of a respiratory condition of the patient using detected SDB events.

1 . A method of one or more processors to screen, diagnose, or monitor sleep disordered breathing (SDB) of a patient, the method comprising:
 extracting a loudness signal from a breathing sound signal of the patient generated by a microphone;   de-rectifying the loudness signal;   detecting SDB events in the de-rectified loudness signal; and   computing a metric of severity of SDB of the patient from the detected SDB events.   
     
     
         2 . The method of  claim 1 , further comprising generating an output based on the metric of severity. 
     
     
         3 . The method of  claim 2 , wherein generating the output comprises comparing the metric of severity with a severity threshold. 
     
     
         4 . The method of  claim 1 , wherein extracting the loudness signal from the breathing sound signal comprises low-pass filtering a root mean square (RMS) value of a window that slides over the breathing sound signal. 
     
     
         5 . The method of  claim 1 , wherein extracting the loudness signal from the breathing sound signal comprises filtering the breathing sound signal to limit included frequencies to a portion of an audio frequency range. 
     
     
         6 . The method of  claim 1 , wherein extracting the loudness signal from the breathing sound signal comprises summing magnitudes of Fourier transform values of the breathing sound signal within a portion of an audio frequency range. 
     
     
         7 . The method of  claim 1 , wherein extracting the loudness signal from the breathing sound signal comprises calculating a power in a resonant frequency range of a window that slides over the breathing sound signal. 
     
     
         8 . The method of  claim 1 , wherein extracting the loudness signal from the breathing sound signal comprises detecting frequency modulation around a basic resonant frequency. 
     
     
         9 . The method of  claim 1 , further comprising filtering the loudness signal to permit an upper frequency that is at two times an upper frequency limit of a human breathing frequency range. 
     
     
         10 . The method of  claim 1 , wherein the SDB events are one or more of:
 apneas;   hypopneas;   periods of Cheyne-Stokes respiration;   snores; and   flow limitations.   
     
     
         11 . The method of  claim 1 , further comprising computing a measure of quality of the loudness signal. 
     
     
         12 . The method of  claim 11 , wherein computing the measure of quality comprises determining whether the loudness signal has most of its power in a human breathing frequency range. 
     
     
         13 . The method of  claim 1 , wherein de-rectifying the loudness signal comprises identifying peaks of the loudness signal and determining which peaks correspond to expiratory portions of a breathing cycle. 
     
     
         14 . The method of  claim 1 , wherein de-rectifying the loudness signal comprises identifying peaks of the loudness signal and determining which peaks correspond to inspiratory portions of a breathing cycle. 
     
     
         15 . The method of  claim 13 , wherein the identifying is based on a duration of a period between at least two successive peaks of the loudness signal. 
     
     
         16 . The method of  claim 15 , wherein when the duration is determined to be shorter than a threshold, (a) an initial peak of the at least two successive peaks is identified as an inspiratory peak, or (b) a following peak of the at least two successive peaks is identified as an expiratory peak. 
     
     
         17 . The method of  claim 16 , wherein the threshold comprises a duration of another period between successive peaks, and wherein the another period precedes or follows the period. 
     
     
         18 . The method of  claim 13 , wherein the identifying is based on a shape of the peaks of the loudness signal, wherein an expiratory peak is more exponentially decaying than an inspiratory peak. 
     
     
         19 . The method of  claim 13 , wherein the identifying is based on frequency content of the peaks of the loudness signal. 
     
     
         20 . The method of  claim 1 , further comprising generating a clip location indicator on a display coupled to the one or more processors, wherein the clip location indicator indicates a location on the display where attachment of a clip permits alignment between a channel of the clip and the microphone. 
     
     
         21 . A processor-readable medium, having stored thereon processor-executable instructions which, when executed by a processor of a portable computing device, cause the processor to screen, diagnose, or monitor sleep disordered breathing (SDB) of a patient, the processor-executable instructions comprising:
 instructions to extract a loudness signal from a breathing sound signal of the patient generated by a microphone;   instructions to de-rectifying the loudness signal;   instructions to detect SDB events in the de-rectified loudness signal; and   instructions to compute a metric of severity of SDB of the patient from the detected SDB events.   
     
     
         22 . A portable computing device comprising: the processor-readable medium of  claim 21 , a microphone, a display and one or more processors configured to access the processor-readable medium to execute the processor-executable instructions of the the processor-readable medium to screen, diagnose, or monitor sleep disordered breathing (SDB) of the patient. 
     
     
         23 . A server with access to the processor-readable medium of  claim 21 , wherein the server is configured to receive requests for downloading the processor-executable instructions of the processor-readable medium to the portable computer device over a network. 
     
     
         24 . A method of a server having access to the processor-readable medium of  claim 21 , the method comprising receiving, at the server, a request for downloading the processor-executable instructions of the processor-readable medium to a portable computer device over a network; and transmitting the processor-executable instructions to the portable computer device in response to the request. 
     
     
         25 . A method of one or more processors to estimate a respiratory flow rate signal from a breathing sound signal of a patient, the method comprising:
 extracting a loudness signal from the breathing sound signal, wherein the breathing sound signal is generated by a microphone; and   de-rectifying the loudness signal to estimate the respiratory flow rate signal of the patient.   
     
     
         26 . The method of  claim 25 , wherein extracting the loudness signal from the breathing sound signal comprises low-pass filtering a root mean square (RMS) value of a window that slides over the breathing sound signal. 
     
     
         27 . The method of  claim 25 , wherein extracting the loudness signal from the breathing sound signal comprises filtering the breathing sound signal to limit included frequencies to a portion of an audio frequency range. 
     
     
         28 . The method of  claim 25 , wherein extracting the loudness signal from the breathing sound signal comprises summing magnitudes of Fourier transform values of the breathing sound signal within a portion of an audio frequency range. 
     
     
         29 . The method of  claim 25 , wherein extracting the loudness signal from the breathing sound signal comprises calculating a power in a resonant frequency range of a window that slides over the breathing sound signal. 
     
     
         30 . The method of  claim 25 , wherein extracting the loudness signal from the breathing sound signal comprises detecting frequency modulation around a basic resonant frequency. 
     
     
         31 . The method of  claim 25 , further comprising filtering the loudness signal to permit an upper frequency that is at two times an upper limit of a human breathing frequency range. 
     
     
         32 . The method of  claim 25 , wherein de-rectifying the loudness signal comprises identifying peaks of the loudness signal and determining which peaks correspond to expiratory portions of a breathing cycle. 
     
     
         33 . The method of  claim 25 , wherein de-rectifying the loudness signal comprises identifying peaks of the loudness signal and determining which peaks correspond to inspiratory portions of a breathing. 
     
     
         34 . The method of  claim 32 , wherein the identifying is based on a duration of a period between at least two successive peaks of the loudness signal. 
     
     
         35 . The method of  claim 34 , wherein when the duration is determined to be shorter than a threshold, (a) an initial peak of the at least two successive peaks is identified as an inspiratory peak or (b) a following peak of the at least two successive peaks is identified as an expiratory peak. 
     
     
         36 . The method of  claim 35 , wherein the threshold comprises a duration of another period between successive peaks, and wherein the another period precedes or follows the period. 
     
     
         37 . The method of  claim 32 , wherein the identifying is based on a shape of the peaks of the loudness signal, wherein an expiratory peak is more exponentially decaying than an inspiratory peak. 
     
     
         38 . The method of  claim 32 , wherein the identifying is based on frequency content of the peaks of the loudness signal. 
     
     
         39 . The method of  claim 25 , further comprising generating a clip location indicator on a display coupled to the one or more processors, wherein the clip location indicator indicates a location on the display where attachment of a clip permits alignment between a channel of the clip and the microphone. 
     
     
         40 . Apparatus comprising:
 means for extracting a loudness signal from a breathing sound signal of a patient; and   means for de-rectifying the loudness signal to estimate a respiratory flow rate signal of the patient.   
     
     
         41 . The apparatus of  claim 40 , further comprising means for conducting breathing sounds from nares of the patient to a microphone of a portable computing device.