Heart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed signal processing or machine learning (ML), or because they indirectly inferred HRV, or because there lacks large training datasets. Many prior studies may also require large ML models. The low accuracy and large model sizes limit their applications to small embedded devices and potential future use in healthcare. To address the above issues, we first collected a large dataset of PPG signals and HRV ground truth. With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV. Evaluation results show that our method had errors between 3.5% to 25.7% and outperformed signal-processing-only and ML-only methods. We also explored different ML models, which showed that Decision Trees and Multi-level Perceptrons have 13.0% and 9.1% errors on average with models at most hundreds of KB and inference time less than 1ms. Hence, they are more suitable for small embedded devices and potentially enable the future use of PPG-based HRV monitoring in healthcare.

翻译：心率变异性（HRV）衡量连续心跳间期的时间变化，是身心健康的重要指标。近期研究表明，光电容积描记术（PPG）传感器可用于推断HRV。然而，许多先前研究存在较高误差，原因包括仅采用信号处理或机器学习（ML）、间接推断HRV，或缺乏大规模训练数据集。部分研究还需构建大型ML模型。低精度与大模型规模限制了其在小型嵌入式设备中的应用及未来医疗领域的潜在价值。针对上述问题，我们首先收集了包含PPG信号与HRV真值的大规模数据集。基于该数据集，我们开发了融合信号处理与ML直接推断HRV的模型。评估结果显示，本方法误差范围为3.5%至25.7%，优于纯信号处理与纯ML方法。我们还探索了不同ML模型，表明决策树与多层感知机平均误差分别为13.0%和9.1%，模型体积最大仅数百KB，推理时间不足1ms。因此，这两种模型更适合小型嵌入式设备，有望推动基于PPG的HRV监测在医疗领域的未来应用。