Design and construction of a pulmonary auscultation system as a support tool in the objective respiratory diseases diagnosis

The objective of the present work is the implementation of a Deep Learning approach using convolutional neural networks that automatically detects the presence of crackles or wheezes, anomalous respiratory sounds present during the inspiration and expiration. In addition, a proposal of a capture mechanism composed by an electret microphone and an acoustic coupler is presented. All of this, with the purpose of devising a support tool in the early respiratory disease diagnosis. Among the main conclusions, it was found that the most appropriate indicator for the model evaluation was sensitivity, where a value of 94,17% was obtained for the validation set, which shows an adequate performance. Additionally, the highest number of erroneous classifications occurred with the crackles, while the lowest in the wheezes, concluding that the system is more effective detecting the second kind of sound. In perspective, the development of a classification algorithm is proposed by taking advantage of frequential and temporal analysis, it manages to find the frequency range and respiratory cycle stage where the anomalous sound happened with the purpose of reaching more specific and precise diagnoses.