A two-branch framework for blood pressure estimation using photoplethysmography signals with deep learning and clinical prior physiological knowledge.

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This paper presents a novel dual-branch framework for estimating blood pressure (BP) using photoplethysmogram (PPG) signals. The method combines deep learning with clinical prior knowledge and models different time periods (morning, afternoon, and evening) to achieve precise, cuffless BP estimation. Preprocessed single-channel PPG signals are input into two feature extraction branches. The first branch converts PPG dimensions to 2D and uses pre-trained MobileViTv2 and Vgg19 backbones to extract deep PPG features based on the different mechanisms of SBP and DBP formation. The second branch calculates multi-dimensional feature parameters based on the relationship between PPG waveforms and factors affecting BP.We fuse the features from both branches and consider diurnal BP variations, using AutoML strategy to construct personalized SBP and DBP estimation models for the different periods. The algorithm was developed on the HRSD dataset and validated on the MIMIC-IV dataset for generalization performance. The mean absolute error (MAE) for BP estimation is 6.42 mmHg (SBP) and 4.96 mmHg (DBP) in the morning, 4.84 mmHg (SBP) and 3.73 mmHg (DBP) in the afternoon, and 2.65 mmHg (SBP) and 2.56 mmHg (DBP) in the evening. Performance on the MIMIC-IV database was 4.34 mmHg (SBP) and 3.11 mmHg (DBP). The method meets the standards of the Association for the Advancement of Medical Instrumentation (AAMI) and achieves Grade A of the British Hypertension Society (BHS) standards. This indicates that it is an accurate and reliable non-invasive BP monitoring technology, applicable for continuous health monitoring and cardiovascular disease prevention.