Cuff-less Blood Pressure Measurement Based on Deep Convolutional Neural Network

Abstract

Cuff-less blood pressure (BP) monitoring is increasingly being needed for cardiovascular events management in clinical. Many of the existing methods, however, are based on manual feature extraction, which cannot characterize the complex relationship between the physiological signals and BP. In this study, the 16-layer VGGNet was used to construct cuff-less BP from electrocardiogram (ECG) and pressure pulse wave (PPW) signals, with no need extract features from raw signals. The deep network architecture has the ability of automatic feature learning, and the learned features are the higher-level abstract description of low-level raw physiological signals. Eight-nine middle-aged and elderly subjects were enrolled to evaluate the performance of the proposed BP estimation method, with oscillometric technique-based BP as a reference. Experimental results indicate that the proposed method had a commendable accuracy in BP estimation, with a correlation coefficient of 0.91 and an estimation error of -2.06 ± 6.89 mmHg for systolic BP, and 0.89 and -4.66 ± 4.91 mmHg for diastolic BP. This study shows that the proposed method provided a potential novel insight for the cuff-less BP estimation.