Developing a multi-feature fusion model for exacerbation classification in asthma and COPD

BACKGROUND AND OBJECTIVE: Deteriorations in respiratory health, also known as exacerbations, are important events in the progression of chronic respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD) and asthma. Changes in vocal characteristics during episodes of respiratory distress suggest that voice analysis could be a valuable tool for monitoring exacerbations. This study aims to develop a remote monitoring method for automatically detecting exacerbations in COPD and asthma patients using only speech data. METHODS: This study proposes a speech-based approach for remote monitoring of asthma and COPD exacerbations, leveraging optimized Mel-Frequency Cepstral Coefficients (MFCC) alongside multi-domain acoustic features. We demonstrate that the optimized MFCC outperforms state-of-the-art feature extraction techniques, while integrating complementary features from the time, frequency, energy, and spectral domains further enhances predictive accuracy. To ensure model transparency and facilitate clinical adoption, we employ SHapley Additive exPlanations (SHAP) to identify key speech biomarkers contributing to exacerbation detection. RESULTS: Compared with the state-of-the-art methods, our method exhibits excellent classification performance with an accuracy of 0.892 and an AUC of 0.955 on the TACTICAS dataset. Moreover, the most salient features ranked by SHAP values are MFCC-related features and energy features, which explains the reason behind the improvement observed with feature fusion. CONCLUSIONS: Comprehensive experiments and comparisons with existing algorithms highlight the potential of speech-based monitoring for respiratory conditions in real-world settings. The proposed method outperforms state-of-the-art approaches, offering a promising avenue for exacerbation diagnosis and monitoring while potentially reducing the burden on both patients and healthcare providers.