Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

2026-02-23

AI Summary

This study addresses the challenge of effectively monitoring dynamic clinical states in heart failure patients using voice-based biomarkers, a task hindered by substantial inter-subject acoustic variability that limits traditional cross-subject classification models. To overcome this, the authors propose a Longitudinal Intra-Patient Tracking (LIPT) framework, which introduces a novel paradigm centered on intra-individual longitudinal vocal changes. The approach employs a Personalized Sequence Encoder (PSE) to transform a patient’s longitudinal voice recordings into context-aware latent representations, enabling dynamic modeling of clinical trajectory evolution. Evaluated on a cohort of 225 patients, the method achieves 99.7% accuracy in detecting clinical state transitions, substantially outperforming conventional cross-sectional models and demonstrating high sensitivity and strong potential for remote, at-home monitoring applications.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Life-Long and Continual Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• heart failure
• speech dynamics
• telemonitoring
• inter-individual heterogeneity
• longitudinal monitoring

Innovation

• Longitudinal Intra-Patient Tracking
• Personalised Sequential Encoder
• speech dynamics
• heart failure telemonitoring
• context-aware latent representations