Muscle Mass Measurement Using Machine Learning Algorithms with Electrical Impedance Myography

Kuo Sheng Cheng, Ya Ling Su, Li Chieh Kuo, Tai Hua Yang, Chia Lin Lee, Wenxi Chen, Shing Hong Liu

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


Sarcopenia is a wild chronic disease among elderly people. Although it does not entail a life-threatening risk, it will increase the adverse risk due to the associated unsteady gait, fall, fractures, and functional disability. The import factors in diagnosing sarcopenia are muscle mass and strength. The examination of muscle mass must be carried in the clinic. However, the loss of muscle mass can be improved by rehabilitation that can be performed in non-medical environments. Electronic impedance myography (EIM) can measure some parameters of muscles that have the correlations with muscle mass and strength. The goal of this study is to use machine learning algorithms to estimate the total mass of thigh muscles (MoTM) with the parameters of EIM and body information. We explored the seven major muscles of lower limbs. The feature selection methods, including recursive feature elimination (RFE) and feature combination, were used to select the optimal features based on the ridge regression (RR) and support vector regression (SVR) models. The optimal features were the resistance of rectus femoris normalized by the thigh circumference, phase of tibialis anterior combined with the gender, and body information, height, and weight. There were 96 subjects involved in this study. The performances of estimating the MoTM used the regression coefficient (r2 ) and root-mean-square error (RMSE), which were 0.800 and 0.929, and 1.432 kg and 0.980 kg for RR and SVR models, respectively. Thus, the proposed method could have the potential to support people examining their muscle mass in non-medical environments.

出版狀態Published - 2022 4月 1

All Science Journal Classification (ASJC) codes

  • 分析化學
  • 資訊系統
  • 生物化學
  • 原子與分子物理與光學
  • 儀器
  • 電氣與電子工程


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