Abstract

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Title:: Non-invasive arterial blood pressure waveform reconstruction algorithm based on Bi-UNet

Author(s):: PAN Jiating1; LIANG Lishi2; CHEN Zhencheng1; 2; 3; 4; 1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China 2. School of Life and Environmental Science, Guilin University of Electronic Technology, Guilin 541000, China 3. Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin 541000, China 4. Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin 541000, China

Keywords:: Keywords: signal reconstruction non-invasive arterial blood pressure waveform photoplethysmogram deep learning

Abstract:: 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.