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

Fan Wu; Matthias P. Nägele; Daryush D. Mehta; Elgar Fleisch; Frank Ruschitzka; Andreas J. Flammer; Filipe Barata

cs.SDMar 31, 2026

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

Authors: Fan Wu, Matthias P. Nägele, Daryush D. Mehta, Elgar Fleisch, Frank Ruschitzka, Andreas J. Flammer, Filipe Barata

Organizations: Centre for Digital Health Interventions, ETH Zurich, Zurich, Switzerland · Agentic System Lab, ETH Zurich, Zurich, Switzerland · Cardiology, University Heart Center Zurich, University Hospital Zurich, Zurich, Switzerland · Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston, MA ·

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 & tables

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.

Vowel LLD features
Feature Type	Features
Vocal-Source-Related LLDs (Phonation / Voice Quality)	Log HNR; jitter (local); shimmer (local); probability of voicing; glottal features (e.g., open quotient, amplitude quotient)
Vocal-Tract-Related LLDs (Articulation / Spectral)	MFCC 1–14; CPP; formants (F1, F2); spectrum bands 1–26 (0–8 kHz); spectral energy 250–650 Hz, 1–4 kHz; spectral roll-off points 0.25, 0.50, 0.75, 0.90; spectral flux, centroid, entropy, slope, variance, skewness, kurtosis; psychoacoustic sharpness, harmonicity
Prosodic LLDs	Sum of auditory spectrum (loudness); F0 (pitch); RMS energy; zero-crossing rate
Speech features
Feature Type	Features

Table 1: Baseline Demographics and Clinical Characteristics (N = 32)

Variable	V1	V2	S
Age (years)	$55.69\pm 11.28$
Sex	Male (90.62%), Female (9.38%)
Smoking	Stopped (78.12%), Yes (12.5%), Never (9.38%)
Height (cm)	$173.78\pm 9.23$
Native German speaker	Yes (81.25%), No (18.75%)
Last LVEF (%)	$27.89\pm 9.07$

Table 2: Baseline Demographics and Clinical Characteristics (N = 32)

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.

Feature	Best states	Worst states	Effect size
Phonation ratio (Text)	0.914 (0.048)	0.826 (0.158)	0.846
Speaking rate (Text)	4.061 (0.231)	3.347 (0.958)	0.805
F1 Std (Command)	16.771 (6.895)	29.279 (15.859)	0.702
Articulation rate (Text)	4.446 (0.325)	4.078 (0.644)	0.662
Shimmer Q3 (/O/)	8.309e $-$ 07 (7.032e $-$ 07)	2.000e $-$ 06 (2.771e $-$ 06)	0.638
F1 FFT1 (Command)	70.583 (39.714)	118.773 (63.202)	0.626

Table 3: Statistical comparison of top features between best and worst health states.

Figure 6: Feature illustration and deterioration prediction for the case study patient. (a) Key feature fluctuations (weight, diastolic pressure, text speaking rate) with mean values per lookback window K = 9 days (orange) and KCCQ scores (dark red). (b) Predicted deterioration probabilities (blue) versus KCCQ trajectory (black), with deteriorating (red) and healthy (green) regions; KCCQ axis reversed so upward indicates deteriorating.

Clinical Outcome Prediction

Clinical outcome prediction uses patient records, imaging, signals, and biomarkers to estimate risks, disease trajectories, treatment response, and survival.

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Remote Patient Monitoring Clinical Speech Processing Latent Dynamics Modeling

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Remote monitoring of heart failure (HF) via speech signals provides a non-invasive and cost-effective solution for long-term patient management. However, substantial inter-individual heterogeneity in vocal characteristics often limits the accuracy of traditional cross-sectional classification models. To address this, we propose a Longitudinal Intra-Patient Tracking (LIPT) scheme designed to capture the trajectory of relative symptomatic changes within individuals. Central to this framework is a Personalised Sequential Encoder (PSE), which transforms longitudinal speech recordings into context-aware latent representations. By incorporating historical data at each timestamp, the PSE facilitates a holistic assessment of the clinical trajectory rather than modelling discrete visits independently. Experimental results from a cohort of 225 patients demonstrate that the LIPT paradigm significantly outperforms the classic cross-sectional approaches, achieving a recognition accuracy of 99.7% for clinical status transitions. The model's high sensitivity was further corroborated by additional follow-up data, confirming its efficacy in predicting HF deterioration and its potential to secure patient safety in remote, home-based settings. Furthermore, this work addresses the gap in existing literature by providing a comprehensive analysis of different speech task designs and acoustic features. Taken together, the superior performance of the LIPT framework and PSE architecture validates their readiness for integration into long-term telemonitoring systems, offering a scalable solution for remote heart failure management.

Abstract

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