RESPIRATION RATE DETECTION METHODOLOGY FOR NEBULIZERS

1 - 22 . (canceled)

23 . A method of detecting a rate of progression of lung, throat, and/or heart pathology, the method comprising:

inputting a plurality of audio files comprising a training set into a deep learning process, wherein the plurality of audio files includes sessions with subjects with known pathologies of varying degrees of severity;

annotating the plurality of audio files with metadata associated with the subjects with known pathologies;

analyzing the plurality of audio files to extract a plurality of audio respiratory signals;

training the deep learning process using the plurality of audio respiratory signals and the metadata;

capturing an audio respiratory signal generated by a subject using a microphone;

segmenting the audio respiratory signal into a plurality of overlapping frames;

for each frame of the plurality of overlapping frames, performing the following:

(i) extracting a signal envelope,

(ii) computing an auto-correlation function,

(iii) computing an FFT spectrum from the auto-correlation function,

(iv) computing a respiratory rate of the subject using the FFT spectrum, and

(v) storing respiratory rates for the plurality of overlapping frames in computer memory;

inputting the stored respiratory rates of the subject into the deep learning process; and

outputting the rate of progression of the lung, throat, and/or heart pathology of the subject using the deep learning process.

24 . The method of claim 23 , further comprising:

updating the deep learning process based on new audio respiratory signals generated by new subjects.

25 . The method of claim 23 , wherein the deep learning process comprises a trained artificial neural network or a convolutional neural network.

26 . The method of claim 23 , wherein each of the plurality of overlapping frames has a duration of at least 30 seconds.

27 . The method of claim 23 , wherein two or more frames of the plurality of overlapping frames overlap by at least 66%.

28 . The method of claim 23 , wherein computing the auto-correlation function comprises:

filtering the auto-correlation function using low and high possible respiratory threshold values.

29 . The method of claim 23 , wherein computing the auto-correlation function comprises:

filtering the auto-correlation function using a high-pass filter.

30 . The method of claim 23 , wherein computing the respiratory rate comprises:

determining a location of a peak magnitude of the FFT spectrum; and

computing one or more values associated with the respiratory rate using the peak magnitude.

31 . The method of claim 30 , further comprising:

applying median filtering to the one or more values associated with the respiratory rate to reduce inaccurate values.

32 . 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 carry out operations to detect a rate of progression of lung, throat, and/or heart pathology, the operations including:

inputting a plurality of audio files comprising a training set into a deep learning process, wherein the plurality of audio files includes sessions with subjects with known pathologies of varying degrees of severity;

annotating the plurality of audio files with metadata associated with the subjects with known pathologies;

analyzing the plurality of audio files to extract a plurality of audio respiratory signals;

training the deep learning process using the plurality of audio respiratory signals and the metadata;

capturing an audio respiratory signal generated by a subject using a microphone;

segmenting the audio respiratory signal into a plurality of overlapping frames;

for each frame of the plurality of overlapping frames performing the following:

(i) extracting a signal envelope,

(ii) computing an auto-correlation function,

(iii) computing an FFT spectrum from the auto-correlation function,

(iv) computing a respiratory rate of the subject using the FFT spectrum, and

(v) storing respiratory rates for the plurality of overlapping frames in computer memory;

inputting the stored respiratory rates of the subject into the deep learning process; and

outputting the rate of progression of the lung, throat, and/or heart pathology of the subject using the deep learning process.

33 . The non-transitory computer-readable storage medium of claim 32 , wherein the operations further include:

updating the deep learning process based on new audio respiratory signals generated by new subjects.

34 . The non-transitory computer-readable storage medium of claim 32 , wherein the deep learning process comprises a trained artificial neural network or a convolutional neural network.

35 . The non-transitory computer-readable storage medium of claim 32 , wherein each of the plurality of overlapping frames has a duration of at least 30 seconds.

36 . The non-transitory computer-readable storage medium of claim 32 , wherein two or more frames of the plurality of overlapping frames overlap by at least 66%.

37 . The non-transitory computer-readable storage medium of claim 32 , wherein computing the auto-correlation function comprises:

filtering the auto-correlation function using low and high possible threshold respiratory values.

38 . The non-transitory computer-readable storage medium of claim 32 , wherein computing the auto-correlation function comprises:

filtering the auto-correlation function using a high-pass filter.

39 . A system for detecting a rate of progression of lung, throat, and/or heart pathology, the system comprising:

a nebulizer communicatively coupled with a microphone, wherein the microphone is operable to capture audio respiratory signals from a subject;

a memory coupled to the nebulizer and operable to store the audio respiratory signal, wherein the memory further comprises an application for detecting the rate of progression of lung, throat, and/or heart pathology; and

a processor coupled to said memory and said nebulizer, the processor being configured to operate in accordance with said application to:

input a plurality of audio files comprising a training set into a deep learning process, wherein the plurality of audio files includes sessions with subjects with known pathologies of varying degrees of severity;

annotate the plurality of audio files with metadata associated with the subjects with known pathologies;

analyze the plurality of audio files to extract a plurality of audio respiratory signals;

train the deep learning process using the plurality of audio respiratory signals and the metadata;

capture an audio respiratory signal generated by a subject using a microphone;

segment the audio respiratory signal into a plurality of overlapping frames;

for each frame of the plurality of overlapping frames perform the following:

(i) extract a signal envelope,

(ii) compute an auto-correlation function,

(iii) compute an FFT spectrum from the auto-correlation function,

(iv) compute a respiratory rate of the subject using the FFT spectrum, and

(v) store respiratory rates for the plurality of overlapping frames in computer memory;

input the stored respiratory rates of the subject into the deep learning process; and

output the rate of progression of the lung, throat, and/or heart pathology of the subject using the deep learning process.

40 . The system of claim 39 , wherein the microphone, the processor and the memory are integrated with the nebulizer in a single device.

41 . The system of claim 39 , wherein the processor is further configured to operate in accordance with said application to update the deep learning process based on new audio respiratory signals generated by new subjects.

42 . The system of claim 39 , wherein the deep learning process comprises a trained artificial neural network.