{"title":"使用无特征卡尔曼滤波器对经皮左心室辅助装置完全支持条件下的反流进行实时估算。","authors":"Anyun Yin, Biyang Wen, Qilian Xie, Ming Dai","doi":"10.1088/1361-6579/ad3d29","DOIUrl":null,"url":null,"abstract":"OBJECTIVE\nSignificant aortic regurgitation is a common complication following left ventricular assist device (LVAD) intervention, and existing studies have not attempted to monitor regurgitation signals and undertake preventive measures during full support. Regurgitation is an adverse event that can lead to inadequate left ventricular unloading, insufficient peripheral perfusion, and repeated episodes of heart failure. Moreover, regurgitation occurring during full support due to pump position displacement cannot be directly controlled through control algorithms. Therefore, accurate estimation of regurgitation during percutaneous left ventricular assist device (PLVAD) full support is critical for clinical management and patient safety.\n\n\nAPPROACH\nAn estimation system based on the regurgitation model is built in this paper, and the unscented Kalman filter estimator (UKF) is introduced as an estimation approach. Three offset degrees and three heart failure states are considered in the investigation. Using the mock circulatory loop (MCL) experimental platform, compare the regurgitation estimated by the UKF algorithm with the actual measured regurgitation; the errors are analyzed using standard confidence intervals of ±2 SDs, and the effectiveness of the mentioned algorithms is thus assessed. The generalization ability of the proposed algorithm is verified by setting different heart failure conditions and different rotational speeds. The root mean square error and correlation coefficient between the estimated and actual values are quantified and the source of the error is illustrated using one-way analysis of variance(One-Way ANOVA), which in turn assessed the accuracy and stability of the UKF algorithm.\n\n\nMAIN RESULTS\nThe research findings demonstrate that the regurgitation estimation system based on the regurgitation model and UKF can relatively accurately estimate the regurgitation status of patients during PLVAD full support, but the effect of cardiac contractility on the estimation accuracy still needs to be taken into account.\n\n\nSIGNIFICANCE\nThe proposed estimation method in this study provides essential reference information for clinical practitioners, enabling them to promptly manage potential complications arising from regurgitation. By sensitively detecting LVAD adverse events, valuable insights into the performance and reliability of the LVAD device can be obtained, offering crucial feedback and data support for device improvement and optimization. .","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"146 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time regurgitation estimation in percutaneous left ventricular assist device fully supported condition using an unscented Kalman filter.\",\"authors\":\"Anyun Yin, Biyang Wen, Qilian Xie, Ming Dai\",\"doi\":\"10.1088/1361-6579/ad3d29\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"OBJECTIVE\\nSignificant aortic regurgitation is a common complication following left ventricular assist device (LVAD) intervention, and existing studies have not attempted to monitor regurgitation signals and undertake preventive measures during full support. Regurgitation is an adverse event that can lead to inadequate left ventricular unloading, insufficient peripheral perfusion, and repeated episodes of heart failure. Moreover, regurgitation occurring during full support due to pump position displacement cannot be directly controlled through control algorithms. Therefore, accurate estimation of regurgitation during percutaneous left ventricular assist device (PLVAD) full support is critical for clinical management and patient safety.\\n\\n\\nAPPROACH\\nAn estimation system based on the regurgitation model is built in this paper, and the unscented Kalman filter estimator (UKF) is introduced as an estimation approach. Three offset degrees and three heart failure states are considered in the investigation. Using the mock circulatory loop (MCL) experimental platform, compare the regurgitation estimated by the UKF algorithm with the actual measured regurgitation; the errors are analyzed using standard confidence intervals of ±2 SDs, and the effectiveness of the mentioned algorithms is thus assessed. The generalization ability of the proposed algorithm is verified by setting different heart failure conditions and different rotational speeds. The root mean square error and correlation coefficient between the estimated and actual values are quantified and the source of the error is illustrated using one-way analysis of variance(One-Way ANOVA), which in turn assessed the accuracy and stability of the UKF algorithm.\\n\\n\\nMAIN RESULTS\\nThe research findings demonstrate that the regurgitation estimation system based on the regurgitation model and UKF can relatively accurately estimate the regurgitation status of patients during PLVAD full support, but the effect of cardiac contractility on the estimation accuracy still needs to be taken into account.\\n\\n\\nSIGNIFICANCE\\nThe proposed estimation method in this study provides essential reference information for clinical practitioners, enabling them to promptly manage potential complications arising from regurgitation. By sensitively detecting LVAD adverse events, valuable insights into the performance and reliability of the LVAD device can be obtained, offering crucial feedback and data support for device improvement and optimization. .\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":\"146 1\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-6579/ad3d29\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6579/ad3d29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Real-time regurgitation estimation in percutaneous left ventricular assist device fully supported condition using an unscented Kalman filter.
OBJECTIVE
Significant aortic regurgitation is a common complication following left ventricular assist device (LVAD) intervention, and existing studies have not attempted to monitor regurgitation signals and undertake preventive measures during full support. Regurgitation is an adverse event that can lead to inadequate left ventricular unloading, insufficient peripheral perfusion, and repeated episodes of heart failure. Moreover, regurgitation occurring during full support due to pump position displacement cannot be directly controlled through control algorithms. Therefore, accurate estimation of regurgitation during percutaneous left ventricular assist device (PLVAD) full support is critical for clinical management and patient safety.
APPROACH
An estimation system based on the regurgitation model is built in this paper, and the unscented Kalman filter estimator (UKF) is introduced as an estimation approach. Three offset degrees and three heart failure states are considered in the investigation. Using the mock circulatory loop (MCL) experimental platform, compare the regurgitation estimated by the UKF algorithm with the actual measured regurgitation; the errors are analyzed using standard confidence intervals of ±2 SDs, and the effectiveness of the mentioned algorithms is thus assessed. The generalization ability of the proposed algorithm is verified by setting different heart failure conditions and different rotational speeds. The root mean square error and correlation coefficient between the estimated and actual values are quantified and the source of the error is illustrated using one-way analysis of variance(One-Way ANOVA), which in turn assessed the accuracy and stability of the UKF algorithm.
MAIN RESULTS
The research findings demonstrate that the regurgitation estimation system based on the regurgitation model and UKF can relatively accurately estimate the regurgitation status of patients during PLVAD full support, but the effect of cardiac contractility on the estimation accuracy still needs to be taken into account.
SIGNIFICANCE
The proposed estimation method in this study provides essential reference information for clinical practitioners, enabling them to promptly manage potential complications arising from regurgitation. By sensitively detecting LVAD adverse events, valuable insights into the performance and reliability of the LVAD device can be obtained, offering crucial feedback and data support for device improvement and optimization. .
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.