MobSpiro: Mobile based spirometry for detecting COPD

摘要

Chronic respiratory diseases are diseases of the airways and other structures of the lung, usually resulting in difficulty in breathing and other symptoms. Chronic obstructive pulmonary disease (COPD) is considered to be one of the most common of respiratory diseases. Nowadays, spirometry remains the golden standard for diagnosing and staging COPD. By taking into consideration the possibility of this disease worsening over time and its negative impact on patient's life, sufferers should conduct regular spirometry checks at medical centers or buy expensive and portable devices to monitor and manage the disease. Therefore, COPD spirometry is costly in terms of both, money and time. In this work and due to the pervasiveness and advancement of smartphones, we attempt to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile based spirometer capable of diagnosing and managing COPD in a reliable and cost effective manner. We develop a model that allows the computation of the two critical parameters FVC and FEV1 by establishing a relationship between the frequency response of human exhalation recorded by mobile microphone and the actual flow rate. These two parameters are critical in diagnosing COPD. Preliminary results show that the mean percent error between FVC, FEV1, and FEV1/FVC ratio data as computed using MobSpiro application and the clinical spirometer is 4.6%, 3.1%, and 3.5% respectively. These results prove the effectiveness of the proposed system when compared to the clinical spirometer, and confirm that smartphones can play an important role in healthcare in the coming future.