Weicai Long
ABSTRACT
We consider the problem of blood pressure estimation. Given a certain physiological signal of the human body, the goal is to estimate the blood pressure. Existing works in this area only use physiological signals and do not utilize personal information such as gender and age. In this paper, we propose a method for blood pressure estimation that uses the Electrocardiogram (ECG), Photoplethysmography (PPG) and personal information to estimate the current blood pressure of the human body. Firstly, we use the slope sum function (SSF) to detect the peaks and valleys of the PPG signal, which lays the foundation for the signal segmentation later. Then, we use two sub-networks to extract features of physiological signals and personal information, respectively. The physiological signal branch uses a residual network with large convolutional kernel attention. Then, we set up a shared space for parameter sharing and subsequently estimated the systolic blood pressure (SBP) and diastolic blood pressure (DBP) values separately using a multi-task structure. Finally, on the MIMIC-III dataset of 795 patients, our method obtained a mean error (ME) ± standard deviation (STD) of 0.15 ± 5.05 mmHg for systolic blood pressure and -0.03 ± 3.31 mmHg for diastolic blood pressure, respectively, meeting international standards.
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Index Terms
- Blood Pressure Estimation Neural Network using Large Kernel Convolutional Attention
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