一种基于端到端神经网络的连续血压估计模型

A Continuous Blood Pressure Estimation Model Based on End-to-End Neural Network

传统基于脉搏波传导时间法及脉搏波特征参数法的血压测量模型存在精度较低及普适性差等不足。构建一种新的连续血压估计模型,通过自动提取必要的波形形态特征及其时域变化,以无创连续的方式估计血压,其由两个层次组成,较低层次使用人工神经网络从光电容积脉搏波(PPG)和心电图(ECG)波形中提取必要的形态特征,较高层次使用长短期记忆网络层来说明较低层次提取特征的时域变化。依据医疗器械发展协会标准,对69名受试者的采样数据进行模型评估,实验结果证明,与基于ECG和PPG特征参数的Deep-RNN血压估计模型相比,该模型具有更高的预测精度。

The traditional Blood Pressure(BP)measurement model based on pulse wave transit time method and pulse wave characteristic parameter method has disadvantages such as low accuracy.This paper proposes a new continuous blood pressure estimation model.The model can automatically extract the necessary features and their time-domain changes,and reliably estimate blood pressure in a non-invasive and continuous manner.The model consists of two layers.The lower layer uses Artificial Neural Network(ANN)to extract necessary morphological features from Electrocardiogram(ECG)and photo Photoplethysmographic(PPG)waveforms.The higher layer uses the Long Short-Term Memory(LSTM)network layer to account for the time domain changes of the features extracted by the lower layer.The proposed model is evaluated on 69 subjects under the standard of the Association for the Advancement of Medical Instrumentations(AAMI).Experimental results show that the proposed model has higher prediction accuracy than Deep-RNN and other BP estimation models based on ECG and PPG feature parameters.