A Non-Invasive Blood Pressure Estimation Method Based on Mamba-UNet and PPG Signals

Continuous blood pressure monitoring is of great significance for the early diagnosis of cardiovascular diseases. To address the limitations of current machine learning and deep learning-based blood pressure (BP) prediction methods, which rely on manual feature extraction and struggle to reconstruct complete BP waveforms, this paper employs a continuous non-invasive arterial BP detection model named Mamba-UNet based on photoplethysmography (PPG) signals. The model deeply integrates the selective state space model (Selective SSM) with the U-Net architecture, achieving direct mapping from PPG signals to arterial blood pressure (ABP) waveforms through end-to-end modeling. In the encoder, the MambaConvBlock module captures long-term temporal dependencies and individual vascular characteristics of PPG signals by dynamically adjusting parameters (∆, B, C). The decoder employs a hybrid Mamba-convolution structure, combining the global dynamic modeling capability of SSM with the local feature extraction ability of convolution to accurately reconstruct BP waveform details. The model design balances the multi-scale feature integration advantages of U-Net with the efficient long-sequence processing capability of Mamba. Evaluated on the Sensors dataset (derived from MIMIC-III, containing 1,131 ICU patient records), Mamba-UNet achieved a mean absolute error (MAE) of 6.06 mmHg for diastolic blood pressure (DBP) and 13.11 mmHg for systolic blood pressure (SBP), outperforming models such as MLP, ResNet, and U-Net.