Continuous Blood Pressure Estimation using 1D Convolutional Neural Network and Attention Mechanism

Abstract

Patients with hypertensive blood pressure (BP) needs a round-the-clock BP monitoring and must take precautions to prevent emergencies such as stroke or heart failure. This paper suggests a deep neural network (DNNs–based BP estimation approach using electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals. The proposed approach consists of a one-dimensional convolutional neural network (1D CNN) followed by the attention mechanism known as Luong attention. Estimations under the proposed model yield mean absolute error (MAE) of 3.299±2.419 for systolic and 2.69±1.821 for diastolic BP. The algorithm can effectively predict BP without a recurrent neural network (RNNs), which is a typical DNNs model for processing sequential data. Additionally, the proposed approach is preferable owing to its ability to explain the model.