Intelligent wearable devices with audio collection capabilities to assess chronic obstructive pulmonary disease severity

Abstract

Background: Intelligent wearable devices have potential for chronic obstructive pulmonary disease (COPD) monitoring, but the effectiveness of combining cough and blowing sounds for disease assessment is unclear. Objective: The objective was to assess COPD severity via physiological parameters and audio data collected by a smartwatch. Methods: COPD patients underwent lung function tests, electrocardiograms, blood gas analysis, and 6-min walk tests. The patients’ peripheral arterial oxygen saturation (SpO2), heart rate variability (HRV), heart rate (HR), and respiratory rate (RR) were continuously monitored via a smartwatch for 7–14 days, and voluntary cough and forceful blowing sounds were recorded twice daily. The HR, SpO2, and RR were categorized into all-day, sleep, and wake periods and summarized using the mean, standard deviation, median, 25th percentile, 75th percentile and percent variation. The correlations among lung function, physiological parameters, and audio data were analyzed to develop a model for predicting COPD severity. Results: Twenty-nine stable patients, with a mean age of 67.0 ± 5.8 years, were enrolled, and 89.7% were male. HR, HRV, RR, cough sounds, and blowing sounds were significantly correlated with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade, with cough sounds showing the highest correlation (r = 0.7617, p