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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.