MuFuBP-Net: A Multimodal Fusion Network for Cuffless Blood Pressure Estimation Using Dual-Feature Pipeline With Probabilistic Feature Encoder

Cuffless blood pressure (BP) estimation is critical for managing growing concerns about hypertension and cardiovascular diseases. Despite recent advancements in multimodal (ECG and PPG) BP estimation methods, which have achieved varying degrees of success, several challenges remain to be addressed. These include capturing the full spectrum of BP-relevant information, redundant feature spaces, and handling the multigrade classification. To address these issues, we propose a Multimodal Fusion BP Network (MuFuBP-Net), featuring a novel dual-feature pipeline architecture designed to extract hierarchical and modality-specific features from both ECG and PPG signals. Additionally, the Cascading Cross-Feature Enhancer (CCFE) module integrates multiple fusion strategies with a squeeze-and-excitation mechanism to apply channel-wise attention to spatial features, enabling dynamic re-weighting. We also employed a Sequence Context Network (SCN) module to capture global sequential features. Subsequently, a Probabilistic Feature Encoder (PFE) encodes the multilevel features from both pipelines into a compact latent space, preserving their discriminative characteristics. Our approach achieved MAE $\pm$ SDE of 2.99 $\pm$ 4.37 mmHg (SBP) and 2.63 $\pm$ 4.19 mmHg (DBP) on MIMIC-II, and 2.27 $\pm$ 4.15 mmHg (SBP) and 1.63 $\pm$ 2.96 mmHg (DBP) on MIMIC-III dataset, meeting AAMI, BHS, and IEEE grade A standards. The proposed approach demonstrated competitive results compared to existing techniques, highlighting its significance as a reliable solution for cuffless BP monitoring.