Calibration-free Blood Pressure Assessment Using An Integrated Deep Learning Method

Abstract

Blood pressure is a key indicator of personal health. In this paper, we propose a novel integrated deep learning method which can accurately determine blood pressure levels under inter-subject scenario without initial calibration. In detail, a convolutional neural network is first introduced to extract features from the raw photoplethysmogram signals. After that, we concatenate the obtained features with personal BMI information and use them as the input of two independent neural networks, which output the estimated blood pressure values and the predicted hypertension class, respectively. These two outputs, in the end, are integrated assessed to generate the final result. Comprehensive experiments demonstrate that our method achieves highly competitive performance compared with others.