Non-invasive arterial blood pressure waveform reconstruction algorithm based on Bi-UNet
10.3969/j.issn.1005-202X.2024.01.010
- VernacularTitle:基于Bi-UNet的无创动脉血压波形重建算法
- Author:
Jiating PAN
1
;
Lishi LIANG
;
Zhencheng CHEN
Author Information
1. 桂林电子科技大学电子工程与自动化学院,广西桂林 541000
- Keywords:
signal reconstruction;
non-invasive;
arterial blood pressure waveform;
photoplethysmogram;
deep learning
- From:
Chinese Journal of Medical Physics
2024;41(1):66-71
- CountryChina
- Language:Chinese
-
Abstract:
A non-invasive deep learning method is proposed for reconstructing arterial blood pressure signals from photoplethysmography signals.The method employs U-Net as a feature extractor,and a module referred to as bidirectional temporal processor is designed to extract time-dependent information on an individual model basis.The bidirectional temporal processor module utilizes a BiLSTM network to effectively analyze time series data in both forward and backward directions.Furthermore,a deep supervision approach which involves training the model to focus on various aspects of data features is adopted to enhance the accuracy of the predicted waveforms.The differences between actual and predicted values are 2.89±2.43,1.55±1.79 and 1.52±1.47 mmHg on systolic blood pressure,diastolic blood pressure and mean arterial pressure,respectively,suggesting the superiority of the proposed method over the existing techniques,and demonstrating its application potential.
