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.
Abstract (translated)
目的：本研究旨在评估哪些语音特征能够预测慢性心力衰竭（HF）患者的健康恶化情况。背景：心力衰竭（HF）是一种慢性进行性疾病，常出现急性失代偿，需频繁住院，造成巨大的医疗和经济负担。当前标准护理（SoC）的家庭监测（如体重追踪）预测准确性有限且需患者高度配合。语音作为一种非侵入性生物标志物具有潜力，但既往研究多聚焦于急性HF阶段。方法：在一项为期2个月的纵向研究中，32名HF患者每日居家录制语音并记录标准护理指标（体重、血压），每两周填写健康状况问卷。通过声学分析生成详细的元音及语音特征，并从聚合回溯窗口（如7天）中提取时序特征以预测次日健康状况。采用可解释机器学习结合嵌套交叉验证筛选关键语音生物标志物，并通过案例研究展示模型应用。结果：共分析21,863条录音。声学元音特征与健康状况呈强相关性。回溯窗口内的时序语音特征表现优于标准护理指标，其峰值敏感度和特异度分别达0.826和0.782，而标准护理指标仅为0.783和0.567。识别健康恶化的关键预后语音特征包括：元音能量延迟偏移、低能量变异度、更高抖动变异度，以及言语中说话/ articulation速率降低、发声时间比减少、音质下降和共振峰变异度增加。结论：基于语音的监测为慢性HF早期健康变化检测提供了非侵入性方案，有助于实现主动式个性化医疗。
URL
https://arxiv.org/abs/2604.00308
