Cardiovascular diseases, including Heart Failure (HF), remain a leading global cause of mortality, often evading early detection. In this context, accessible and effective risk assessment is indispensable. Traditional approaches rely on resource-intensive diagnostic tests, typically administered after the onset of symptoms. The widespread availability of electrocardiogram (ECG) technology and the power of Machine Learning are emerging as viable alternatives within smart healthcare. In this paper, we propose several multi-modal approaches that combine 30-s ECG recordings and approximate long-term Heart Rate Variability (HRV) data to estimate the risk of HF hospitalization. We introduce two survival models: an XGBoost model with Accelerated Failure Time (AFT) incorporating comprehensive ECG features and a ResNet model that learns from the raw ECG. We extend these with our novel long-term HRVs extracted from the combination of ultra-short-term beat-to-beat measurements taken over the day. To capture their temporal dynamics, we propose a survival model comprising ResNet and Transformer architectures (TFM-ResNet). Our experiments demonstrate high model performance for HF risk assessment with a concordance index of 0.8537 compared to 14 survival models and competitive discrimination power on various external ECG datasets. After transferability tests with Apple Watch data, our approach implemented in the App offers cost-effective and highly accessible HF risk assessment, contributing to its prevention and management.

中文翻译：

---

基于短心电图和采样长期 HRV 的多模式心力衰竭风险评估

包括心力衰竭 (HF) 在内的心血管疾病仍然是全球主要的死亡原因，而且往往无法及早发现。在这种情况下，方便且有效的风险评估是必不可少的。传统方法依赖于资源密集型诊断测试，通常在症状出现后进行。心电图 (ECG) 技术的广泛应用和机器学习的力量正在成为智能医疗保健领域的可行替代方案。在本文中，我们提出了几种多模式方法，结合 30 秒心电图记录和近似长期心率变异性 (HRV) 数据来估计心力衰竭住院风险。我们引入了两种生存模型：具有加速故障时间 (AFT) 的 XGBoost 模型，结合了全面的 ECG 功能，以及从原始 ECG 中学习的 ResNet 模型。我们通过从当天进行的超短期逐搏测量组合中提取的新颖的长期 HRV 来扩展这些。为了捕捉它们的时间动态，我们提出了一个由 ResNet 和 Transformer 架构组成的生存模型（TFM-ResNet）。我们的实验证明了心力衰竭风险评估的高模型性能，与 14 个生存模型相比，一致性指数为 0.8537，并且在各种外部心电图数据集上具有竞争辨别力。经过 Apple Watch 数据的可转移性测试后，我们在应用程序中实施的方法提供了经济高效且易于访问的心力衰竭风险评估，有助于其预防和管理。