PPG-Based Continuous BP Waveform Estimation Using Polarized Attention-Guided Conditional Adversarial Learning Model

The blood pressure (BP) waveform is a vital source of physiological and pathological information concerning the cardiovascular system. This study proposes a novel attention-guided conditional generative adversarial network (cGAN), named PPG2BP-cGAN, to estimate BP waveforms based on photoplethysmography (PPG) signals. The proposed model comprises a generator and a discriminator. Specifically, the UNet3+-based generator integrates a full-scale skip connection structure with a modified polarized self-attention module based on a spatial-temporal attention mechanism. Additionally, its discriminator comprises PatchGAN, which augments the discriminative power of the generated BP waveform by increasing the perceptual field through fully convolutional layers. We demonstrate the superior BP waveform prediction performance of our proposed method compared to state-of-the-art (SOTA) techniques on two independent datasets. Our approach first pre-trained on a dataset containing 683 subjects and then tested on a public dataset. Experimental results from the Multi-parameter Intelligent Monitoring in Intensive Care dataset show that the proposed method achieves a root mean square error of 3.54, mean absolute error of 2.86, and Pearson coefficient of 0.99 for BP waveform estimation. Furthermore, the estimation errors (mean error ± standard deviation error) for systolic BP and diastolic BP are 0.72 ± 4.34 mmHg and 0.41 ± 2.48 mmHg, respectively, meeting the American Association for the Advancement of Medical Instrumentation standard. Our approach exhibits significant superiority over SOTA techniques on independent datasets, thus highlighting its potential for future applications in continuous cuffless BP waveform measurement.