PPG-FusionNet: A Dual-Branch Neural Architecture for Cuffless Blood Pressure Estimation from Photoplethysmography

Abstract

Hypertension is a major risk factor for cardiovascular disease, yet cuffless blood pressure monitoring remains challenging because most existing methods rely on intermittent cuff-based measurements or multimodal physiological signals. This work proposes PPG-FusionNet, a dual-branch deep learning architecture for simultaneous systolic and diastolic blood pressure estimation using only photoplethysmography (PPG) signals. The model combines a local dilated convolutional encoder and a global AutoCorrelation encoder operating on a shared patch embedding, whose representations are integrated through cross-attention fusion and optimized with a constrained dual-head regression objective. The model was trained and evaluated on the PulseDB benchmark using Bayesian hyperparameter optimization and systematic ablation studies to assess each architectural component. PPG-FusionNet achieved mean absolute errors of 7.46 mmHg for systolic blood pressure and 4.72 mmHg for diastolic blood pressure, with near-zero mean errors and compliance with the ANSI/AAMI standard and BHS Grade B for diastolic estimation. Ablation experiments revealed that trend-seasonal decomposition, despite its success in long-horizon forecasting, degraded performance on short PPG windows, whereas cross-attention fusion and AutoCorrelation improved estimation accuracy. These results demonstrate that heterogeneous dual-branch representation learning provides an effective, scalable framework for cuffless blood pressure estimation from a single PPG sensor.