Ho Chih-I, Yen Chia-Hsiang, Li Yu-Chuan, Huang Chiu-Hua, Guo Jia-Wei, Tsai Pei-Yun, Lin Hung-Ju, Wang Tzung-Dau
Department of Electrical Engineering, National Central University, Taoyuan, Taiwan.
Graduate School of Advanced Technology, National Taiwan University, Taipei, Taiwan.
JMIR Biomed Eng. 2025 Aug 26;10:e58756. doi: 10.2196/58756.
Photoplethysmography (PPG) signals captured by wearable devices can provide vascular age information and support pervasive and long-term monitoring of personal health condition.
In this study, we aimed to estimate brachial-ankle pulse wave velocity (baPWV) from wrist PPG and electrocardiography (ECG) from smartwatch.
A total of 914 wrist PPG and ECG sequences and 278 baPWV measurements were collected via the smartwatch from 80 men and 82 women with average age of 63.4 (SD 13.4) and 64.3 (SD 11.6) years. Feature extraction and weighted pulse decomposition were applied to identify morphological characteristics regarding blood volume change and component waves in preprocessed PPG and ECG signals. A systematic strategy of feature combination was performed. The hierarchical regression method based on the random forest for classification and extreme gradient boosting (XGBoost) algorithms for regression was used, which first classified the data into subdivisions. The respective regression model for the subdivision was constructed with an overlapping zone.
By using 914 sets of wrist PPG and ECG signals for baPWV estimation, the hierarchical regression model with 2 subdivisions and an overlapping zone of 400 cm per second achieved root-mean-square error of 145.0 cm per second and 141.4 cm per second for 24 men and 26 women, respectively, which is better than the general XGBoost regression model and the multivariable regression model (all P<.001).
We for the first time demonstrated that baPWV could be reliably estimated by the wrist PPG and ECG signals measured by the wearable device. Whether our algorithm could be applied clinically needs further verification.
可穿戴设备采集的光电容积脉搏波描记法（PPG）信号能够提供血管年龄信息，并支持对个人健康状况进行普及性长期监测。
在本研究中，我们旨在通过智能手表的腕部PPG和心电图（ECG）来估计臂踝脉搏波速度（baPWV）。
通过智能手表收集了80名男性和82名女性的914组腕部PPG和ECG序列以及278次baPWV测量值，这些参与者的平均年龄分别为63.4岁（标准差13.4）和64.3岁（标准差11.6）。应用特征提取和加权脉搏分解来识别预处理后的PPG和ECG信号中有关血容量变化和成分波的形态特征。实施了特征组合的系统策略。使用基于随机森林的分层回归方法进行分类，并使用极端梯度提升（XGBoost）算法进行回归，该方法首先将数据分类为细分。为细分构建了各自的回归模型，并设置了一个重叠区域。
通过使用914组腕部PPG和ECG信号进行baPWV估计，具有2个细分且重叠区域为每秒400厘米的分层回归模型在24名男性和26名女性中分别实现了均方根误差为每秒145.0厘米和每秒141.4厘米，这优于一般的XGBoost回归模型和多变量回归模型（所有P<0.001）。
我们首次证明，可通过可穿戴设备测量的腕部PPG和ECG信号可靠地估计baPWV。我们的算法是否可应用于临床需要进一步验证。
