计算机工程与应用2024，Vol.60Issue(4)：324-330,7.DOI:10.3778/j.issn.1002-8331.2211-0031
采用融合的U-net模型连续无创动脉血压预测方法
Predication Method of Continuous Non-Invasive Arterial Blood Pressure Using Fusion U-net Model
摘要
Abstract
Continuous blood pressure monitoring is helpful to the diagnosis and treatment of cardiovascular diseases.At present,machine learning and deep learning are used to predict blood pressure by manually extracting feature parameters.This method cannot reconstruct complete blood pressure signals.Therefore,a continuous non-invasive arterial blood pres-sure measurement method based on the fused U-net model is proposed.Firstly,the original photoplethysmogram(PPG)signal is used as the input to reduce the error of manually extracting feature parameters.Secondly,the U-net network is used to reconstruct the arterial blood pressure signal.In order to further improve the accuracy of the predicted blood pres-sure waveform,the reconstructed blood pressure signal is used as the input of the MultiResUnet network.The MultiRes module is used to learn different features from the data.The Res Path module alleviates the semantic differences between the encoder and the decoder,making the model learning easier.The arterial blood pressure(ABP)waveform predicted by the fused U-net network in the subject evaluation of MIMIC-Ⅲ dataset is highly correlated with the actual waveform.The calculated mean absolute errors of systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean pressure(MAP)are 2.20±4.30 mmHg,1.82±3.146 mmHg and 2.25±2.86 mmHg.The method satisfies the requirements of the Association for the Advancement of Medical Instrumentation(AAMI)standard and reaches Grade A in the British High Pressure Society(BHS)standard.关键词
动脉血压(ABP)/电容积脉搏波(PPG)/无创/U-netKey words
arterial blood pressure(ABP)/photoplethysmogram(PPG)/non-invasive/U-net分类
信息技术与安全科学引用本文复制引用
王军昂,张立新,王赛,吴凯枫,阚希,陈乃源..采用融合的U-net模型连续无创动脉血压预测方法[J].计算机工程与应用,2024,60(4):324-330,7.基金项目
国家自然科学基金青年科学基金(42105143) （42105143）
江苏省教育厅高等学校基础科学(自然科学)研究面上项目(580221016). （自然科学）
