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[2602.19674] Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics
Summary
This article presents a novel approach for continuous telemonitoring of heart failure through personalized speech dynamics, showcasing significant advancements in remote patient management.
Why It Matters
As heart failure remains a leading cause of morbidity, innovative monitoring solutions like the proposed Longitudinal Intra-Patient Tracking (LIPT) can enhance patient outcomes by providing real-time insights into health status, thereby improving clinical decision-making and patient safety.
Key Takeaways
- The LIPT framework significantly improves monitoring accuracy by tracking individual patient speech dynamics over time.
- A Personalised Sequential Encoder (PSE) effectively captures context-aware representations of speech data.
- The model achieved a remarkable 99.7% accuracy in recognizing clinical status transitions among heart failure patients.
- This approach addresses the limitations of traditional cross-sectional models by focusing on longitudinal data.
- Integration of this technology could revolutionize remote heart failure management and enhance patient safety.
Computer Science > Sound arXiv:2602.19674 (cs) [Submitted on 23 Feb 2026] Title:Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics Authors:Yue Pan, Xingyao Wang, Hanyue Zhang, Liwei Liu, Changxin Li, Gang Yang, Rong Sheng, Yili Xia, Ming Chu View a PDF of the paper titled Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics, by Yue Pan and 8 other authors View PDF HTML (experimental) 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 s...
