IMSF-Net:An improved multi-scale information fusion network for PPG-based blood pressure estimation

Abstract

Recently, deep learning (DL) architectures have been widely used for PPG-based blood pressure (BP) monitoring due to their powerful feature extraction ability. However, the DL methods still suffer from limitations. First, the multi-scale time dependency characteristics of PPG require the DL-based methods to have the ability of sufficient feature representation at various scales. Second, the lack of DL model interpretability can lead to concerns about the reliability and generalizability of prediction. In this study, a new PPG-based end-to-end deep learning method, referred to as the IMSF-Net, is proposed for BP estimation, and its advantages are as follows: (1) the improved multi-scale fusion (IMSF) block is designed to extract different-scale feature in a two-channel way, which can obtain the abstract high-level information while avoiding low-level information loss simultaneously; (2) Permutation importance algorithm is firstly adopted to determine what parts of PPG are most useful for automatic BP estimation. On the University of California, Irvine database, the proposed IMSF-Net achieves mean absolute error (MAE) ± standard deviation (STD) of 2.07 ± 2.32 mmHg for systolic blood pressure (SBP) and 1.21 ± 1.51 mmHg for diastolic blood pressure (DBP). On the healthy database, referred to as the VDB, the method achieves MAE ± STD of 1.75 ± 3.98 mmHg for SBP and 1.29 ± 2.3 mmHg for DBP. Both of the performances outperform related previous works. The model interpretation results show that feature grids comprising of Diastolic 1–3, and Systolic 5, which generally cover the waveform from the peak point to the dicrotic notch, have a strong correlation with BP estimation.