TY - GEN
T1 - Respiratory Sounds Classification employing a Multi-label Approach
AU - Romero Gómez, Andrés Felipe
AU - Orjuela-Cañón, Alvaro D.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/26
Y1 - 2021/5/26
N2 - Respiratory diseases are one of the leading causes of death worldwide, which also reduces the quality of life of the people who suffer from them.. Therefore, there is the necessity to generate tools that allow a rapid and reliable diagnosis support to make an appropriate management of these diseases. Different approaches based on artificial intelligence (AI) have been contributed to these problems, which has shown to be useful in assisting the diagnosis of such diseases. The present proposal holds the use of AI algorithms to identify respiratory sounds that are associated with respiratory diseases (crackles and wheezes), for this, the database Respiratory Sound Database from the ICBHI 2017 Challenge was employed. The proposed models use statistics of time and frequency features, and a multi-label approach for the classification, which is a different approach from that used in related work, where a multi-target approach is employed. As a result, three machine learning algorithms were trained for both a multi-label and a multi-class classification, obtaining comparable results between them. For the case of the multi-label models we obtained at most an average output accuracy of 81.9%.
AB - Respiratory diseases are one of the leading causes of death worldwide, which also reduces the quality of life of the people who suffer from them.. Therefore, there is the necessity to generate tools that allow a rapid and reliable diagnosis support to make an appropriate management of these diseases. Different approaches based on artificial intelligence (AI) have been contributed to these problems, which has shown to be useful in assisting the diagnosis of such diseases. The present proposal holds the use of AI algorithms to identify respiratory sounds that are associated with respiratory diseases (crackles and wheezes), for this, the database Respiratory Sound Database from the ICBHI 2017 Challenge was employed. The proposed models use statistics of time and frequency features, and a multi-label approach for the classification, which is a different approach from that used in related work, where a multi-target approach is employed. As a result, three machine learning algorithms were trained for both a multi-label and a multi-class classification, obtaining comparable results between them. For the case of the multi-label models we obtained at most an average output accuracy of 81.9%.
UR - http://www.scopus.com/inward/record.url?scp=85114213658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114213658&partnerID=8YFLogxK
U2 - 10.1109/ColCACI52978.2021.9469042
DO - 10.1109/ColCACI52978.2021.9469042
M3 - Conference contribution
AN - SCOPUS:85114213658
T3 - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings
BT - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021 - Proceedings
A2 - Orjuela-Canon, Alvaro David
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2021
Y2 - 26 May 2021 through 28 May 2021
ER -
