A Deep Learning Approach for Non-Invasive Hypertension Classification from PPG Signal

Hypertension or high blood pressure is the leading risk factor with the largest contribution to the burden of disease and mortality. Therefore, regular blood pressure (BP) checks are necessary to track hypertension and reduce risk. In light of this, we have proposed a noninvasive method for hypertension detection based on photoplethysmography (PPG) signals using deep learning techniques. A hybrid deep learning model consisting of a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is introduced. First, PPG signals were acquired from 219 subjects, which underwent pre-processing steps. Then, hypertension was detected with satisfactory results by using our proposed model. Here, CNN is used to extract features in order to work better in classification tasks. Our proposed model constructed with CNN and GRU achieved an Accuracy of 85.00 %, Recall of 84.44 %, Precision of 87.77% and F1-score of 86.08%. Our goal is to improve the medical industry by precisely detecting hypertension.