Cuff-Less Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Based on VGG19-LSTM Network

Abstract

In recent years, studies have found that the hierarchical neural network with LSTM network has higher accuracy than another feature engineering. Therefore, this paper first tries to build a multi-stage blood pressure estimation model through VGG19 and LSTM network. Based on the time node of the R wave peak in the QRS waveform in ECG, VGG19 is used to extract various higher-dimensional and rich life characteristics in the PPG signal segment by heartbeat as the unit and focus on processing the dynamics of SBP and DBP Correlation, finally use the LSTM model to extract the time dependence of the vital signs. Results: Experiments show that compared with similar multi-stage models, this model has higher accuracy. The performance of this method meets the Advancement of Medical Instrumentation (AAMI) standard and reaches the A level of the British Hypertension Society (BHS) standard. The average error and standard deviation of the estimated value of SBP were 1.7350 4.9606 mmHg, and the average error and standard deviation of the estimated value of DBP were 0.7839 2.7700 mmHg, respectively.