Detecting COPD Exacerbations Before Onset Using Vocal Biomarkers

Abstract:
Early detection of Chronic Obstructive Pulmonary Disease exacerbations (ECOPDs) is essential for timely intervention and improved disease management. The prodromal phase, marked by subtle physiological changes before clinical diagnosis, presents an opportunity for non-invasive detection but has not been explored yet. This study examines acoustic biomarkers from sustained vowel and text-reading tasks as potential early indicators of ECOPDs. Using a longitudinal dataset of voice recordings from 19 patients, we captured 21 ECOPD events. Our machine learning model detected 79% of ECOPDs up to three days before onset, demonstrating the potential of vocal biomarkers for early warning. While promising, further research and larger datasets are needed to enhance model performance. Future work will focus on longitudinal trend analysis to improve predictive accuracy and interpretability.