基于卷积神经网络-长短期记忆神经网络模型利用光学体积描记术重建动脉血压波信号

Arterial Blood Pressure Wave Signal Reconstruction Using Photoplethysmography by CNN-LSTM Model

目的直接动脉血压(arterial blood pressure,ABP)连续监测是侵入式的,传统袖带式的间接血压测量法无法实现连续监测。既往利用光学体积描记术(photoplethysmography,PPG)实现了连续无创血压监测,但其为收缩压和舒张压的离散值,而非ABP波的连续值,本研究期望基于卷积神经网络-长短期记忆神经网络(CNN-LSTM)利用PPG信号波重建ABP波信号,实现连续无创血压监测。方法构建CNN-LSTM混合神经网络模型,利用重症监护医学信息集(medical information mart for intensive care,MIMIC)中的PPG与ABP波同步记录信号数据,将PPG信号波经预处理降噪、归一化、滑窗分割后输入该模型,重建与之同步对应的ABP波信号。结果使用窗口长度312的CNN-LSTM神经网络时,重建ABP值与实际ABP值间误差最小,平均绝对误差(mean absolute error,MAE)和均方根误差(root mean square error,RMSE)分别为2.79 mmHg和4.24 mmHg,余弦相似度最大,重建ABP值与实际ABP值一致性和相关性情况良好,符合美国医疗器械促进协会(Association for the Advancement of Medical Instrumentation,AAMI)标准。结论CNN-LSTM混合神经网络可利用PPG信号波重建ABP波信号,实现连续无创血压监测。

Objective Direct continuous monitoring of arterial blood pressure is invasive and continuous monitoring cannot be achieved by traditional cuffed indirect blood pressure measurement methods.Previously,continuous non-invasive arterial blood pressure monitoring was achieved by using photoplethysmography(PPG),but it is discrete values of systolic and diastolic blood pressures rather than continuous values constructing arterial blood pressure waves.This study aimed to reconstruct arterial blood pressure wave signal based on CNN-LSTM using PPG to achieve continuous non-invasive arterial blood pressure monitoring.Methods A CNN-LSTM hybrid neural network model was constructed,and the PPG and arterial blood pressure wave synchronized recorded signal data from the Medical Information Mart for Intensive Care(MIMIC)were used.The PPG signals were input to this model after noise reduction,normalization,and sliding window segmentation.The corresponding arterial blood pressure waves were reconstructed from PPG by using the CNN-LSTM hybrid model.Results When using the CNN-LSTM neural network with a window length of 312,the error between the reconstructed arterial blood pressure values and the actual arterial blood pressure values was minimal:the values of mean absolute error(MAE)and root mean square error(RMSE)were 2.79 mmHg and 4.24 mmHg,respectively,and the cosine similarity is the optimal.The reconstructed arterial blood pressure values were highly correlated with the actual arterial blood pressure values,which met the Association for the Advancement of Medical Instrumentation(AAMI)standards.Conclusion CNN-LSTM hybrid neural network can reconstruct arterial blood pressure wave signal using PPG to achieve continuous non-invasive arterial blood pressure monitoring.