Vocal Prognostic Digital Biomarkers in Monitoring Chronic Heart Failure: A Longitudinal Observational Study

Abstract

Objective: This study aimed to evaluate which voice features can predict health deterioration in patients with chronic HF. Background: Heart failure (HF) is a chronic condition with progressive deterioration and acute decompensations, often requiring hospitalization and imposing substantial healthcare and economic burdens. Current standard-of-care (SoC) home monitoring, such as weight tracking, lacks predictive accuracy and requires high patient engagement. Voice is a promising non-invasive biomarker, though prior studies have mainly focused on acute HF stages. Methods: In a 2-month longitudinal study, 32 patients with HF collected daily voice recordings and SoC measures of weight and blood pressure at home, with biweekly questionnaires for health status. Acoustic analysis generated detailed vowel and speech features. Time-series features were extracted from aggregated lookback windows (e.g., 7 days) to predict next-day health status. Explainable machine learning with nested cross-validation identified top vocal biomarkers, and a case study illustrated model application. Results: A total of 21,863 recordings were analyzed. Acoustic vowel features showed strong correlations with health status. Time-series voice features within the lookback window outperformed corresponding standard care measures, achieving peak sensitivity and specificity of 0.826 and 0.782 versus 0.783 and 0.567 for SoC metrics. Key prognostic voice features identifying deterioration included delayed energy shift, low energy variability, and higher shimmer variability in vowels, along with reduced speaking and articulation rate, lower phonation ratio, decreased voice quality, and increased formant variability in speech. Conclusion: Voice-based monitoring offers a non-invasive approach to detect early health changes in chronic HF, supporting proactive and personalized care.

Figures (6)

• Figure 1: Voice-based monitoring system for chronic HF. We conducted a home-based longitudinal study in which patients recorded daily speech and vowels. To develop and validate vocal biomarkers, we designed a pipeline including acoustic feature extraction, time-series analysis, and explainable machine-learning prediction of health deterioration, resulting in the identification of novel predictive biomarkers. Voice features outperformed SoC measures, highlighting their potential for earlier detection and proactive management to reduce hospitalizations.
• Figure 2: Repeated measures correlation between health status and dynamic features. Health status is represented by the KCCQ overall summary score. Shown are the standard deviations of selected features over a 7-day lookback window. Each color represents one patient. Panels include: (a) max MFCC of vowel /a/; (b) mean pause duration (command task); (c–d) blood pressure; (e) weight; (f) HFaST score. r indicates the correlation coefficient, and p the significance level.
• Figure 3: Classification performance comparison across different feature sets. (a) Sensitivity, (b) Specificity, (c) F1 score, and (d) MCC are reported. Each dot represents the mean performance of a feature set across multiple measurements for a given lookback window (K = 2–14 days). The shaded area shows the SoC performance, used as the baseline for comparison.
• Figure 4: ROC and Precision–Recall curves for different feature sets. (a) AUC and (b) AUPRC are shown to indicate performance in distinguishing health deterioration. Each line represents a specific feature set using a lookback window of K = 9 days.
• Figure 5: SHAP global and summary plots for the voice feature set. The top vowel (a) and speech (b) features are shown, with positive SHAP values indicating contributions toward worse health outcomes. Feature labels are simplified codes; see Supplementary Material for more descriptions.