#Normal and abnormal lung sounds professional#

Thus, the key fundamental, initial step in developing our telemedicine platform is to determine the presence of adventitious sounds and distinguish between four classes of wheeze, crackle, a combination of both, and normal breathing. Besides, most respiratory ailments are accompanied by the same sound anomalies during the patient’s inspiration and/or expiration cycles 23.

In those COVID-19 cases, in which the infection has led to acute respiratory distress 14’ 32, breathing appears as distant sounds accompanied by coarse crackles and diffuse wheezes 5 ‘ 20. Moreover, in a broader context, it can assist pulmonologists to diagnose varying degrees of sound abnormalities and lung ailments including asthma, chronic obstructive pulmonary disease, pneumonia, infection, and inflammation of the airways and facilitate the long-term, remote monitoring and treatments in inexpensive, non-invasive, and safe ways. Our system may provide a better chance of benefiting from treatments as well as preventing the spread of the coronavirus. The current situation has highly motivated us to consider our project as an opportunity to develop a telemedicine platform for remote, automated diagnosis of adventitious pathological sounds which can contribute to diagnosis of respiratory disorders.

#Normal and abnormal lung sounds professional#

Moreover, it emphasized the necessity of telemedicine and smart telediagnosis of disease while the availability of professional physicians are low and the risk of in-person check outs are high. These circumstances that have dramatically impacted all aspects of our lives highlight the importance of early and automated diagnosis of such respiratory disorders more than ever.

Viral pneumonia, difficulty in breathing and dyspnea became known as key symptoms in infected patients and in severe cases caused uncountable deaths since the virus outbreak 32. The emergence of the 2019 novel coronavirus (2019-nCoV) has caused a large global outbreak and major public health issues 15. Respiratory Sounds Classification | CS 7651 - Machine Learning (Team 7) Respiratory Sounds Classification CS 7651 - Machine Learning (Team 7) View on GitHubĪnjana Nandagopal, Elvan Ugurlu, Fereshteh Shahmiri, Serhat Erdogan, Sukris Vong.