Respiration rate detection methodology for nebulizers

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.

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.

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.