Arterial Blood Pressure Wave Signal Reconstruction Using Photoplethysmography by CNN-LSTM Model
10.16476/j.pibb.2022.0574
- VernacularTitle: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
- Author:
Jia-Ze WU
1
;
Hao LIANG
2
;
Ming CHEN
1
Author Information
1. School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 102488, China
2. Institute of TCM Diagnostics, Hunan University of Chinese Medicine, Changsha 410208, China
- Publication Type:Journal Article
- Keywords:
continuous non-invasive blood pressure monitoring;
volume pulse wave;
arterial blood pressure wave;
convolutional neural network;
long short term memory neural network;
hybrid neural network
- From:
Progress in Biochemistry and Biophysics
2024;51(2):447-458
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveDirect 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. MethodsA 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. ResultsWhen 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. ConclusionCNN-LSTM hybrid neural network can reconstruct arterial blood pressure wave signal using PPG to achieve continuous non-invasive arterial blood pressure monitoring.
