Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications

Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. Methods: We proposed an end-to-end data preprocessing framework that adapts multiple pulsatile signal modalities and generates machine-learning-ready datasets agnostic to downstream medical tasks. Results: a dataset preprocessed by Pulse2AI improved systolic blood pressure estimation by 29.58%, from 11.41 to 8.03 mmHg in root-mean-square-error (RMSE) and its diastolic counterpart by 26.01%, from 7.93 to 5.87 mmHg in RMSE. For respiration rate (RR) estimation, Pulse2AI boosted performance by 19.69%, from 1.47 to 1.18 breaths per minute (BrPM) in mean-absolute-error (MAE). Conclusion: Pulse2AI turns pulsatile signals into machine learning (ML) ready datasets for arbitrary remote health monitoring tasks. We tested Pulse2AI on multiple pulsatile modalities and demonstrated its efficacy in two medical applications. This work bridges valuable assets in remote sensing and internet of medical things to ML-ready datasets for medical modeling.

目标：将Pulse2AI确立为一种用于脉搏信号的可重现数据预处理框架，该框架能从原始可穿戴设备记录中生成适合机器学习的高质量数据集。 方法：我们提出了一种端到端的数据预处理框架，它适用于多种脉搏信号模态，并生成与下游医疗任务无关的适合机器学习的数据集。 结果：经Pulse2AI预处理的数据集使收缩压估计值提高了29.58%，均方根误差（RMSE）从11.41 mmHg降至8.03 mmHg，舒张压估计值提高了26.01%，RMSE从7.93 mmHg降至5.87 mmHg。对于呼吸频率（RR）估计，Pulse2AI使性能提高了19.69%，平均绝对误差（MAE）从每分钟1.47次呼吸（BrPM）降至1.18次呼吸。 结论：Pulse2AI将脉搏信号转换为适合机器学习（ML）的数据集，可用于任意远程健康监测任务。我们在多种脉搏模态上对Pulse2AI进行了测试，并在两种医疗应用中证明了其有效性。这项工作将遥感和医疗物联网中的有价值资产与适合机器学习的数据集相连接，用于医疗建模。