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基于希尔伯特-黄变换的肘部僵硬的生物电阻抗谱分析。

Analysis of Bioelectrical Impedance Spectrum for Elbow Stiffness Based on Hilbert-Huang Transform.

机构信息

Department of Spinal Surgery, Affiliated Hospital of Gansu University of Chinese Medicine, Lanzhou, Gansu 730000, China.

College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China.

出版信息

Contrast Media Mol Imaging. 2022 Apr 8;2022:5764574. doi: 10.1155/2022/5764574. eCollection 2022.

DOI:10.1155/2022/5764574
PMID:35480083
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9012657/
Abstract

With the advent of posttraumatic elbow rehabilitation, prevention of elbow stiffness has become a key part of the development of sports medicine. In order to clarify the time point of joint movement after internal fixation to the elbow and to provide a mechanical model for individualized diagnosis. This paper uses electromagnetic wave detection technology to quickly detect the bioelectrical impedance signal of the patient's lesion location, then passes the message to the upper control system for processing, summarizes the improved Hilbert-Huang transform to deep learning, and deep learning algorithms and computer technology are used to mine the bioelectrical impedance signal of the elbow joint. The simulation and human experiment results show that bioelectrical impedance signals can clarify the pathogenesis of elbow joint stiffness and the relationship between rehabilitation treatment time and duration. It has the advantages of low cost, high fitting accuracy, strong robustness, and noninvasiveness.

摘要

随着创伤后肘部康复的出现，预防肘部僵硬已成为运动医学发展的关键部分。为了明确肘部内固定后关节活动的时间点，并为个体化诊断提供力学模型。本文使用电磁波检测技术快速检测患者病变部位的生物电阻抗信号，然后将信息传递给上位控制系统进行处理，总结了改进的 Hilbert-Huang 变换到深度学习，以及深度学习算法和计算机技术用于挖掘肘部关节的生物电阻抗信号。模拟和人体实验结果表明，生物电阻抗信号可以阐明肘部关节僵硬的发病机制以及康复治疗时间与持续时间之间的关系。它具有成本低、拟合精度高、鲁棒性强、非侵入性等优点。

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