Özet:

Periodic monitoring of breath sounds is essential for early screening of obstructive upper respiratory tract infections, such as inflammation of the airway typically caused by viruses. As an immediate first step, there is a need to detect abnormalities in breath sounds. The adult average male lung capacity is approximately 6 liters and the manifestation of pulmonary diseases, unfortunately, remains undetected until their advanced stages when the disease manifests into severe conditions. Additionally, such rapidly progressing conditions, which arise due to viral infections that need to be detected via adventitious breath sounds to take immediate therapeutic action, demand frequent monitoring. These tests are usually conducted by a trained physician by means of a stethoscope, which requires an in-person visit to the hospital. During a pandemic situation such as COVID-19, it is difficult to provide periodic screening of large volumes of people with the existing medical infrastructure. Fortunately, smartphones are ubiquitous, and even developing countries with skewed doctor-to-patient ratios typically have a smartphone in every household. With this technology accessibility in mind, we present a smartphone-based solution that monitors breath sounds from the user via the in-built microphone of their smartphone and our Artificial Intelligence (AI) -based anomaly detection engine. The presented automated classifier for abnormal breathing sounds is able to detect abnormalities in the early stages of respiratory dysfunctions with respect to their individual normal baseline vesicular breath sounds, with an accuracy of 95%, and it can flag them, and thus enhances the possibility of early detection. Keywords: breath sounds; anomaly detection; feature extraction; classification; artificial neural networks; machine learning; convolution neural networks

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