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Robust autoregression with exogenous input model for system identification and predicting

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收录情况: ◇ SCIE ◇ EI ◇ 预警期刊

机构: [1]MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China [2]Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China [3]School of Bioinfomatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China [4]School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China [5]Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China [6]Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China
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关键词: Artifact outliers ARX model EEG Lp (p ≤ 1) norm

摘要:
Autoregression with exogenous input (ARX) is a widely used model to estimate the dynamic relationships between neurophysiological signals and other physiological parameters. Nevertheless, biological signals, such as electroencephalogram (EEG), arterial blood pressure (ABP), and intracranial pressure (ICP), are inevitably contaminated by unexpected artifacts, which may distort the parameter estimation due to the use of the L2 norm structure. In this paper, we defined the ARX in the Lp (p ≤ 1) norm space with the aim of resisting outlier influence and designed a feasible iteration procedure to estimate model parameters. A quantitative evaluation with various outlier conditions demonstrated that the proposed method could estimate ARX parameters more robustly than conventional methods. Testing with the resting‐state EEG with ocular artifacts demonstrated that the proposed method could predict missing data with less influence from the artifacts. In addition, the results on ICP and ABP data further verified its efficiency for model fitting and system identification. The proposed Lp‐ARX may help capture system parameters reliably with various input and output signals that are contaminated with artifacts. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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出版当年[2021]版:
大类 | 4 区 工程技术
小类 | 4 区 计算机:信息系统 4 区 工程:电子与电气 4 区 物理:应用
最新[2025]版:
大类 | 4 区 计算机科学
小类 | 4 区 计算机:信息系统 4 区 工程:电子与电气 4 区 物理:应用
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出版当年[2021]版:
Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Q3 PHYSICS, APPLIED
最新[2024]版:
Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Q3 PHYSICS, APPLIED

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第一作者机构: [1]MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China [2]Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
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通讯机构: [1]MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China [2]Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China [5]Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China [6]Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China
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