Calibration-Based Cuffless Blood Pressure Estimation Using a Dilated-Residual Attention U-Net

Non-invasive blood pressure (BP) monitoring using photoplethysmography (PPG) has significant potential, yet accurately predicting systolic (SBP) and diastolic (DBP) blood pressure using photoplethysmogram (PPG) and electrocardiogram (ECG) signals remains challenging. This work proposes a novel dual-stream 1D encoder–decoder architecture for cuffless BP estimation from raw photoplethysmography (PPG) and electrocardiography (ECG) signals. The model incorporates multi-scale temporal feature extraction via dilated residual convolutions, cross-signal feature modulation at the bottleneck using Feature-wise Linear Modulation (FiLM), learned scale-wise fusion across encoder levels, attention-gated skip connections, and a hybrid mean-attention pooling regression head. Evaluated under calibration-based conditions on the PulseDB dataset (with selected data values of SBP ∈ [57, 180] mmHg and DBP ∈ [25, 100]), the proposed model achieved a mean absolute error (MAE) of 2.03 mmHg, mean error (ME) of 0.38 ± 3.13 mmHg, and an R2 of 0.93 for diastolic BP (DBP) and an MAE of 3.81 mmHg, ME of −0.28 ± 5.54 mmHg, and an R2 of 0.92 for systolic BP (SBP). The results meet the British Hypertension Society (BHS) and IEEE-1708 standard and achieved an “A” Grade. ECG alone provides lower prediction errors than PPG alone under the evaluated conditions, while their combination yields the best performance. Gender- and age-stratified analyses reveal consistent model behavior across demographic subgroups, with prediction error increasing modestly in older cohorts, particularly among women. This study provides an accurate, calibration-based, cuffless BP estimation and highlights its potential for non-invasive BP monitoring applications.