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.

翻译：通过语音信号远程监测心力衰竭（HF）为长期患者管理提供了一种非侵入性且经济高效的解决方案。然而，个体间声学特征的显著异质性常常限制了传统横断面分类模型的准确性。为解决此问题，我们提出了一种纵向患者内追踪（LIPT）方案，旨在捕捉个体内部相对症状变化的轨迹。该框架的核心是一个个性化序列编码器（PSE），它将纵向语音记录转换为上下文感知的潜在表征。通过在每个时间戳纳入历史数据，PSE有助于对临床轨迹进行整体评估，而非独立地对离散的就诊事件进行建模。对225名患者队列的实验结果表明，LIPT范式显著优于经典的横断面方法，在临床状态转变的识别上达到了99.7%的准确率。额外的随访数据进一步证实了该模型的高灵敏度，确认了其在预测HF恶化方面的有效性及其在远程、居家环境中保障患者安全的潜力。此外，本研究通过对不同语音任务设计和声学特征的全面分析，弥补了现有文献中的空白。综上所述，LIPT框架和PSE架构的卓越性能验证了其已具备集成到长期远程监测系统中的条件，为远程心力衰竭管理提供了一个可扩展的解决方案。