Noninvasive Blood Pressure Classification based on ECG with ResNet Algorithm

This study proposes a noninvasive end-to-end blood pressure (BP) classification model based on electrocardiography (ECG), dividing BP into three categories, namely, normotension (N), prehypertension (Pre), and hypertension (H). This study includes three parts: (1) preprocessing ECG signals, including cutting signals into fixed-length segments and denoising; (2) building a proper ResNet model with a 1D convolutional neural network (1D-CNN) to train data; and (3) evaluating the performance of the classification model. The classification algorithm can identify three types of BP, and the accuracy rate of the classification algorithm over the test set is 87.89%.