Using Attention and Multi-Scale Unet Approach to Continuous Blood Pressure Prediction

Blood pressure prediction is a crucial tool in preventing cardiovascular-related diseases. Therefore, improving the accuracy of blood pressure prediction plays a critical role in disease prevention. Previous models for blood pressure prediction have faced challenges related to inadequate feature extraction and insignificant effective information mining. To address these issues, this paper proposes an improved Unet-based continuous blood pressure prediction method that effectively processes the spatial information of multi-scale feature maps and establishes long-term dependencies between multi-scale channels. The proposed model achieves high accuracy meeting the requirements of the AAMI standard and the BHS A Grade. Moreover, the mean absolute errors of systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 3.41 mmHg and 2.58 mmHg, respectively, with standard deviations (STD) of 6.25 mmHg and 5.08 mmHg.