Non-Invasive Continuous Blood Pressure Estimation from Single-Channel PPG Based on a Temporal Convolutional Network Integrated with an Attention Mechanism

作者：Dong Dai, Zhaohui Ji, Haiyan Wang · 发表于：Applied Sciences · 年份：2024 · DOI：10.3390/app14146061 · 被引用次数：16 · 研究领域：Non-Invasive Vital Sign Monitoring、Heart Rate Variability and Autonomic Control、Hemodynamic Monitoring and Therapy

Traditional cuff-based blood pressure measurement methods suffer from issues such as intermittency and applicability, while cuff-less continuous blood pressure estimation techniques are increasingly gaining attention due to their non-invasive and continuous monitoring advantages. In this paper, aiming at the challenges faced by existing cuff-less continuous blood pressure estimation models in terms of accuracy, data requirements, and generalization ability, a series of innovative approaches are proposed. Deep learning techniques are introduced to design an end-to-end blood pressure estimation model with high accuracy, ease of training, and strong generalization ability. To address the insufficient accuracy of traditional neural networks in cuff-less continuous blood pressure estimation, we propose an end-to-end, beat-to-beat blood pressure estimation model that combines the temporal convolutional network (TCN) and convolutional block attention module (CBAM). By enhancing the model’s ability to process time series data and focus on key features of photoplethysmography (PPG), the blood pressure estimation accuracy during the resting state is significantly improved. The absolute mean error and standard deviation of systolic blood pressure (SBP) estimation using the algorithm in this chapter on the University of California, Irvine (UCI) physiological signal dataset are 5.3482 mmHg and 8.3410 mmHg, respectively, which are superior to other deep learning models based on convolution...