Dual path network improves wearable blood pressure estimation accuracy
SIFPBPNet: A Dual-Path Network for Wearable and Cuffless Blood Pressure Estimation via Individualized Steady-state Representation
Machine Learning
Summary
Blood pressure is an important health measure but usually needs a cuff to check, which isn’t convenient all the time. This paper presents a new computer method that uses data from wearable devices to estimate blood pressure without a cuff by learning both steady and changing patterns in the data. Their method also learns from longer-term history to understand personal differences, making it more accurate than existing approaches. The authors tested it on a large dataset and showed it works better for predicting blood pressure continuously and comfortably.
What this means in practice
- •For wearable device developers: Design wearable health monitors that provide more accurate blood pressure estimates by integrating individualized steady-state representations.$Commercial implications: Enables the creation of consumer-friendly wearable blood pressure monitors that improve accuracy and personalization compared to current models.
- •For mobile health app developers: Enhance mobile apps with improved cuffless blood pressure estimation using dual-path PPG analysis for personalized cardiovascular monitoring.
Authors
Shuailong Tang, Xiaoyu Li, Donglin Xie, Wei Chen, Guangpu Zhu, Yelei Li, Yali Zheng
Abstract
Continuous and cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) is of great interest for low-cost and personalized cardiovascular health management. However, significant population heterogeneity and the "one-to-many mapping" problem, where similar waveforms across individuals correspond to different BP levels, limit the accuracy of conventional population-based models. To address this challenge, we propose a dual-path architecture termed SIFPBPNet, which separately represents steady-state and instantaneous features, through a Steady-state Feature Path (SFP) and an Instantaneous Feature Path (IFP). The SFP employs a Graph Attention Network (GAT) to extract individual-specific and long-term characteristics from multi-day historical PPG trajectories. In parallel, the IFP captures short-term dynamics from current PPG segments and incorporates the steady-state prior via a cross-attention mechanism. Experiments on a large-scale wearable dataset demonstrate that SIFPBPNet achieves a Mean Absolute Error (MAE) of 8.57 and 5.97 mmHg for systolic and diastolic BP, respectively, outperforming state-of-the-art models. Furthermore, the SFP module consistently improves performance when integrated into various backbone architectures, yielding 2.8-13.1% relative MAE reductions for systolic BP. These results highlight the strong generalizability and plug-and-play transferability of the SFP module, underscoring its great potential for accurate cuffless BP monitoring.