Estimation of Brachial-Ankle Pulse Wave Velocity With Hierarchical Regression Model From Wrist Photoplethysmography and Electrocardiographic Signals: Method Design.

Abstract

Background: Photoplethysmography (PPG) signals captured by wearable devices can provide vascular age information and support pervasive and long-term monitoring of personal health condition.

Objective: In this study, we aimed to estimate brachial-ankle pulse wave velocity (baPWV) from wrist PPG and electrocardiography (ECG) from smartwatch.

Methods: 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.

Results: 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).

Conclusions: 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.