Automatic Breath Analysis System Using Convolutional Neural Networks

Abstract

Diseases related to the human respiratory system have always been a burden for the entire society. The situation has become particularly difficult now after the outbreak of the COVID-19 pandemic. Even now, however, it is common for people to consult their doctor too late, after the disease has developed. To protect patients from severe disease, it is recommended that any symptoms disturbing the respiratory system be detected as early as possible. This article presents an early prototype of a device that can be compared to a digital stethoscope that performs auto-breath analysis. So apart from recording the respiratory cycles, the device also analyzes them. In addition, it also has the functionality of notifying the user (e.g. via a smartphone) about the need to go to the doctor for a more detailed examination. The audio recording of breath cycles is transformed to a two-dimensional matrix using mel-frequency cepstrum coefficients (MFCC). Such a matrix is analyzed by an artificial neural network. As a result of the research, it was found that the best of the obtained solutions of the presented neural network achieved the desired accuracy and precision at the level of 84%.