Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

TL;DR

This paper introduces a personalized longitudinal speech analysis framework for remote heart failure monitoring, significantly improving accuracy in detecting clinical status changes over traditional methods.

Contribution

It proposes a novel Longitudinal Intra-Patient Tracking scheme with a Personalized Sequential Encoder, enhancing the assessment of individual health trajectories from speech data.

Findings

Achieved 99.7% accuracy in recognizing clinical status transitions.

Demonstrated superior performance over cross-sectional models.

Validated effectiveness in predicting heart failure deterioration.

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.