Abstract: A method determining lung pathology severity from a subject under test includes receiving a training set comprising a plurality of breath flow signals and a plurality of audio signals for a convolutional neural network (CNN). The method includes training a convolutional neural network and creating at least one test graph using a breath flow signal and an audio signal from the subject under test. The method further includes inputting the at least one test graph associated with the subject under test into the CNN and determining an existing pathology and associated severity for the subject under test. Also, the method includes determining a prediction for a future possible condition of the subject and determining the lung pathology severity be computing a distance between the future possible condition of the subject under test and the existing pathology and associated severity.

Abstract: A computer-implemented method for determining lung pathology from audio respiratory and breath flow signals comprises receiving a plurality of breath flow signals and a plurality of audio signals comprising a training set for a convolutional neural network (CNN), wherein the plurality of breath flow signals and the plurality of audio signals are extracted from sessions with patients with known pathologies of known degrees of severity. The method also comprises analyzing the plurality of audio signals and the plurality of breath flow signals, wherein the analyzing comprises extracting a plurality of descriptors associated with the audio and breath flow signals. Further, the method comprises creating a plurality of graphs using information from the descriptors, wherein at least one of the graphs comprises a plot combining descriptors from both the audio and the breath flow signals. The method also comprises training the CNN using the plurality of graphs.

Abstract: A method for detecting wheeze from an audio respiratory signal comprises capturing the audio respiratory signal from a subject using a microphone. Further, the method comprises recognizing a plurality of breath cycles and a plurality of breath phases from the audio respiratory signal and detecting wheezing from the plurality of breath cycles and the plurality of breath phases. The detecting comprises analyzing a block of interest in the audio respiratory signal, wherein the block of interest comprises a plurality of frames. The detecting further comprises calculating an auto-correlation function (ACF) for each frame in the block and determining a maximum value of the ACF calculated for each frame in the block. Finally, the detecting comprises analyzing the maximum value to detect if wheezing is present in the block.

Abstract: A computer-implemented method for determining lung pathology from an audio respiratory signal comprises inputting a plurality of audio files comprising a training set into an artificial neural network (ANN), wherein the plurality of audio files comprise sessions with patients with known pathologies of known degrees of severity. The method further comprises annotating the plurality of audio files with metadata relevant to the patients and the known pathologies and analyzing the plurality of audio files, wherein the analyzing comprises extracting spectrograms for each of the plurality of audio files and a plurality of descriptors associated with wheeze and crackle from the plurality of audio files. Additionally, the method comprises training the ANN using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors. The method finally comprises determining a lung pathology associated with a new sound recording inputted into the ANN.

Abstract: A method for analyzing an audio respiratory signal comprises capturing the audio respiratory signal from a subject using a microphone and partitioning the audio respiratory signal into a plurality of overlapping frames. The method further comprises calculating a fourier transform for each frame and determining a magnitude spectrum using the fourier transform of the plurality of overlapping frames. Additionally, the method comprises extracting a spectrogram using the magnitude spectrum and analyzing the spectrogram to determine characteristics pertaining to wheeze sounds in the audio respiratory signal.

Abstract: A method for determining respiratory rate from an audio respiratory signal comprising capturing the audio respiratory signal generated by a subject using a microphone. The method also comprises segmenting the audio respiratory signal into a plurality of overlapping frames. For each frame of the plurality of overlapping frames, the method comprises extracting a signal envelope, computing an auto-correlation function, computing an FFT spectrum from the auto-correlation function and computing a respiratory rate of the subject using the FFT spectrum.

Abstract: A method for detecting thresholds in a breathing session is disclosed. The method comprises recording breathing sounds of a subject using a microphone. The method further comprises processing the breathing sounds to generate an audio respiratory signal and recognizing a plurality of breath cycles from the audio respiratory signal. Additionally, the method comprises extracting metrics related to a breath intensity and a breath rate from the plurality of breath cycles and producing a plurality of vectors using the metrics related to the breath intensity and the breath rate. Further, the method comprises calculating a master vector by summing the plurality of vectors and assigning each value in the master vector with a weighting coefficient and determining the thresholds using peak values in said master vector.

Abstract: A method for analyzing an audio respiratory signal comprises capturing the audio respiratory signal from a subject using a microphone and partitioning the audio respiratory signal into a plurality of overlapping frames. The method further comprises calculating a fourier transform for each frame and determining a magnitude spectrum using the fourier transform of the plurality of overlapping frames. Additionally, the method comprises extracting a spectrogram using the magnitude spectrum and analyzing the spectrogram to determine characteristics pertaining to wheeze sounds in the audio respiratory signal.

Abstract: A computer-implemented method for determining lung pathology from an audio respiratory signal comprises inputting a plurality of audio files comprising a training set into an artificial neural network (ANN), wherein the plurality of audio files comprise sessions with patients with known pathologies of known degrees of severity. The method further comprises annotating the plurality of audio files with metadata relevant to the patients and the known pathologies and analyzing the plurality of audio files, wherein the analyzing comprises extracting spectrograms for each of the plurality of audio files and a plurality of descriptors associated with wheeze and crackle from the plurality of audio files. Additionally, the method comprises training the ANN using the plurality of audio files, the spectrograms, the metadata and the plurality of descriptors. The method finally comprises determining a lung pathology associated with a new sound recording inputted into the ANN.

Abstract: A method for detecting wheeze from an audio respiratory signal comprises capturing the audio respiratory signal from a subject using a microphone. Further, the method comprises recognizing a plurality of breath cycles and a plurality of breath phases from the audio respiratory signal and detecting wheezing from the plurality of breath cycles and the plurality of breath phases. The detecting comprises analyzing a block of interest in the audio respiratory signal, wherein the block of interest comprises a plurality of frames. The detecting further comprises calculating an auto-correlation function (ACF) for each frame in the block and determining a maximum value of the ACF calculated for each frame in the block. Finally, the detecting comprises analyzing the maximum value to detect if wheezing is present in the block.

Abstract: A method for detecting thresholds in a breathing session is disclosed. The method comprises recording breathing sounds of a subject using a microphone. The method further comprises processing the breathing sounds to generate an audio respiratory signal and recognizing a plurality of breath cycles from the audio respiratory signal. Additionally, the method comprises extracting metrics related to a breath intensity and a breath rate from the plurality of breath cycles and producing a plurality of vectors using the metrics related to the breath intensity and the breath rate. Further, the method comprises calculating a master vector by summing the plurality of vectors and assigning each value in the master vector with a weighting coefficient and determining the thresholds using peak values in said master vector.

Abstract: A method for performing dynamic classification of a breathing session is disclosed. The method comprises capturing breathing sounds of a subject using a microphone. Further, it comprises recognizing a plurality of breath cycles and a plurality of breath phases within each of the plurality of breath cycles from the breathing sounds. It also comprises detecting characteristics regarding the plurality of breath cycles and the plurality of breath phases. Finally, it comprises extracting metrics concerning a breath pattern quality of the subject using the detected characteristics.

Abstract: A method for performing dynamic classification of a breathing session is disclosed. The method comprises capturing breathing sounds of a subject using a microphone. Further, it comprises recognizing a plurality of breath cycles and a plurality of breath phases within each of the plurality of breath cycles from the breathing sounds. It also comprises detecting characteristics regarding the plurality of breath cycles and the plurality of breath phases. Finally, it comprises extracting metrics concerning a breath pattern quality of the subject using the detected characteristics.