An End-to-End Neural Network Model for Blood Pressure Estimation Using PPG Signal

With the increasing number of hypertension patients, the monitoring of blood pressure information becomes an important task. In this study, an end-to-end approach is proposed to estimate blood pressure from the pulse wave signal. In this approach, a normalized single pulse wave is the input of a neural network, which consists of the convolutional layers and the recurrent layers, then outputs the corresponding blood pressure. The convolutional layers consist of one-dimensional convolutional layers and depth-separable convolutional layers. The gated recurrent unit (GRU) is used in the recurrent layer. Finally, a dense layer is used to output estimated values of blood pressure. In comparison with previous approaches, the proposed method does not require complicated feature extraction. It is only necessary to input a single pulse wave into the neural network and blood pressure can be estimated. The proposed approach is tested in the multi-parameter intelligent monitoring in intensive care (MIMIC) dataset, and the average absolute error is 3.95 mmHg for systolic blood pressure and 2.14 mmHg for diastolic blood pressure. This result fulfills the international standard of blood pressure measurement, which shows the proposed approach is simple and effective. In practice, the proposed method is designed to obtain blood pressure information from pulse waves.