用智能手表PPG数据，深度学习识别高血压风险。
Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning
- 用ResNet和Transformer直接分析原始腕部PPG信号。
- 在90人真实连续记录中准确区分健康与异常人群。
- 模型仅12.4万参数，适合可穿戴设备部署。
高血压是心血管疾病的主要风险因素。传统血压监测方法繁琐，难以实现持续跟踪，推动了基于光电容积脉搏波（PPG）的无袖带可穿戴设备发展。本研究采用深度学习模型（包括ResNet和Transformer），分析智能手表采集的腕部PPG数据，实现高效的高血压风险筛查，无需人工设计PPG特征。基于448名受试者的家庭血压监测（HBPM）纵向数据集，通过五折交叉验证，模型在358名受试者超过68,000个点检样本上训练，并在90名受试者的实际连续记录中测试。紧凑的ResNet模型（0.124M参数）显著优于传统机器学习方法，证明其在真实场景下区分健康与异常个体的有效性。
原文摘要 · Abstract (English)
Hypertension is a leading risk factor for cardiovascular diseases. Traditional blood pressure monitoring methods are cumbersome and inadequate for continuous tracking, prompting the development of PPG-based cuffless blood pressure monitoring wearables. This study leverages deep learning models, including ResNet and Transformer, to analyze wrist PPG data collected with a smartwatch for efficient hypertension risk screening, eliminating the need for handcrafted PPG features. Using the Home Blood Pressure Monitoring (HBPM) longitudinal dataset of 448 subjects and five-fold cross-validation, our model was trained on over 68k spot-check instances from 358 subjects and tested on real-world continuous recordings of 90 subjects. The compact ResNet model with 0.124M parameters performed significantly better than traditional machine learning methods, demonstrating its effectiveness in distinguishing between healthy and abnormal cases in real-world scenarios.
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