Quantitative Assessment and Diagnosis of Muscle Function in Sarcopenia Based on EIT-derived Parameters.

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Yanan Diao, Guilan Chen, Junwen Peng, Nan Lou, Bo Sun, Jiafeng Yao, Guanglin Li, Guoru Zhao
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Abstract

The quantitative evaluation and diagnosis of muscle function in patients with sarcopenia are crucial to mitigate functional decline and the health burden in aging populations. This study proposed a method for the classification of sarcopenia and the evaluation of muscle function scores based on EIT technology. We recruited 31 participants, including individuals with sarcopenia (n = 11), healthy elderly (n = 10), and healthy young adults (n = 10), obtained muscle clinical fitness assessment scores and EIT-derived parameters, conducted intergroup comparisons of EIT parameters and clinical scores, and constructed a machine learning classification model for sarcopenia. EIT parameters conductivity (σ) were significantly different among the three groups (p < 0.05). Clinical muscle function scores showed a strong positive correlation with the σ (r = 0.73, R² = 0.54, p < 0.001), while negatively correlated with impedance (Z) (r = -0.55, R² = 0.27, p < 0.05). In addition, σ was positively correlated with hand grip strength (HGS) (r = 0.52, R² =0.20, p=0.30), and maximum voluntary muscle contraction (MVC) (r=0.73, R² = 0.49, p<0.001), and negatively correlated with age (r = -0.76, R² = 0.56, p<0.001) and SARC-F scale scores (r = -0.73, R² =0.57, p<0.001). Finally, the KNN-based sarcopenia classification model demonstrated strong performance in classification tasks, as evidenced by an accuracy of 0.89 and an AUC of 0.94. This study demonstrates that the EIT is a portable, wearable, and long-term monitoring tool for assessing and classifying muscle function in sarcopenia. With further clinical validation, it is expected to be used for early screening and rehabilitation monitoring of sarcopenia.

基于eit衍生参数的肌少症患者肌肉功能定量评估与诊断。
定量评估和诊断肌肉减少症患者的肌肉功能对减轻老年人的功能下降和健康负担至关重要。本研究提出了一种基于EIT技术的肌少症分类及肌肉功能评分评估方法。我们招募了31名参与者,包括肌肉减少症患者(n = 11)、健康老年人(n = 10)和健康年轻人(n = 10),获得了肌肉临床适应度评估得分和EIT衍生参数,并进行了EIT参数和临床评分的组间比较,构建了肌肉减少症的机器学习分类模型。三组间EIT参数电导率(σ)差异有统计学意义(p < 0.05)。临床肌肉功能评分与σ呈显著正相关(r = 0.73, r²= 0.54,p < 0.001),与阻抗(Z)呈显著负相关(r = -0.55, r²= 0.27,p < 0.05)。此外,σ与握力(HGS) (r= 0.52, r²=0.20,p=0.30)、最大随意肌收缩(MVC) (r=0.73, r²= 0.49,p=0.30)呈正相关
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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