What does this research mean for the field?

Voice data recorded via mobile phones capture significant acoustic differences in the vowel [a:] between individuals with COPD and healthy controls, demonstrating the potential of voice as a digital biomarker for noninvasive screening. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 1, 2025Open Access

Feature Analysis of the Vowel a: in Individuals With Chronic Obstructive Pulmonary Disease and Healthy Controls

AIAlper IdrisogluBlekinge Institute of Technology AMAna Luiza Dallora MoraesBlekinge Institute of Technology ACAbbas CheddadBlekinge Institute of Technology

Key Points

Key points are not available for this paper at this time.

Abstract

BACKGROUND: In addition to impairing the lung function, chronic obstructive pulmonary disease (COPD) also affects phonatory characteristics. Recent research highlights the potential of voice as a digital biomarker to support clinical decision-making. While machine learning (ML) can detect disease patterns from acoustic features, clinical relevance requires understanding the relationship between the disorder and acoustic features. OBJECTIVE: This study investigates both statistical and clinical significance using Baseline Acoustic (BLA) and Mel-Frequency Cepstral Coefficient (MFCC) features with focusing on individuals with COPD and healthy controls (HC). METHOD: Acoustic features derived from Swedish utterances of the vowel a:, recorded via mobile phones from 48 age-matched participants (24 COPD, 24 HC; equal gender distribution), were analyzed. To reduce bias from varying recording counts, features were aggregated by averaging 10 randomly selected recordings per participant over 100 iterations. Vowel articulation was visualized in the vowel quadrilateral space using F1 (tongue height) and F2 (tongue advancement). Group differences were assessed using the Shapiro-Wilk test, Mann-Whitney U test (α = 0.05), Benjamini-Hochberg (BH) and Bonferroni corrections, Permutational Multivariate Analysis of Variance (PERMANOVA) test, and Cliff's Delta (δ). RESULTS: Of 101 features, 29 remained significant after BH correction and one after Bonferroni. Multivariate testing (p = 0.019) showed group separation. Additionally, 34 features demonstrated large effect sizes, suggesting potential as digital biomarkers. CONCLUSION: Voice data recorded via mobile phones capture meaningful acoustic differences associated with COPD. These findings support the integration of voice-based assessments into eHealth platforms for noninvasive COPD screening and monitoring, which is pending further validation on larger populations. CLINICAL TRIAL: NCT06705647.

اسأل الذكاء الاصطناعي

Bookmark

View Full Paper