用基础模型预测可穿戴设备心率变异性，提升临床预警能力
Zero-Shot Heart Rate Variability Forecasting from Consumer Wearables Using Time Series Foundation Models
- 引入自适应噪声插补法，保留生理动态特征
- 无需微调即超越传统方法，平均MASE低至0.81
- 适合关注可穿戴健康监测与临床预警的开发者
短期心率变异性（HRV）预测可为临床提供检测自主神经功能障碍和心脏事件的提前预警时间。消费级可穿戴设备产生的HRV信号碎片化且含大量伪影，挑战传统预测方法。本研究在49名健康个体的真实可穿戴数据上，评估了三种时间序列基础模型（TimesFM、Chronos、MOIRAI）的预测性能，对比了均值、指数平滑、加权移动平均等传统基线。为应对数据碎片化问题，提出一种保持变异性特征的插补方法，通过局部自适应随机噪声增强线性插值，保留关键生理动态。结果表明，三种TSFMs均无需微调即优于所有基线，在32和64时间步长下平均绝对缩放误差（MASE）介于0.81至0.87之间，其中Chronos和TimesFM表现最优，而MOIRAI提升有限。在长达2小时的预测时域内，建立了真实数据集上的性能基准，凸显领域微调对临床部署的潜力。
原文摘要 · Abstract (English)
Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events. Consumer wearable devices generate fragmented, artifact-rich HRV signals that challenge conventional forecasting approaches. In this study, we evaluated the forecasting ability of three Time Series Foundation Models (TSFMs), TimesFM, Chronos, and MOIRAI, against traditional baselines (Mean, Exponential Smoothing, and Exponentially Weighted Moving Average) on real-world wearable data collected from 49 healthy individuals. To address data fragmentation, we introduce a variability-preserving imputation method that augments linear interpolation with locally adaptive stochastic noise, retaining physiological dynamics essential for accurate forecasting. The results show that TSFMs outperformed all baselines without fine-tuning, achieving average Mean Absolute Scaled Error (MASE) between 0.81 and 0.87 across TSFMs and both context lengths (32 and 64 time steps), with Chronos and TimesFM as the top models, though MOIRAI showed limited gains over baselines. With up to a 2-hour forecast horizon, the results establish a baseline for TSFMs' performance on a real-world dataset, highlighting domain-specific fine-tuning as a promising direction for clinical deployment.
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