基于盲源分离的肌肉电阻抗分析

Linnan Huang, Dongming Li, Yueming Gao, M. Du
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摘要

电阻抗肌图(EIM)是一种评价神经肌肉疾病和肌肉疲劳的新技术。然而,EIM是肌肉状态的生物标志物,其敏感性受皮肤和脂肪的影响。为了从EIM数据中恢复肌肉层的阻抗,我们使用ICA和EASI算法来估计每个组织层的阻抗,并比较ICA和EASI方法的性能。结果表明,EASI算法能较好地恢复肌肉阻抗,准确率为0.999。本研究为肌肉疾病的治疗和肌肉疲劳的恢复提供了新的思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Muscle Electrical Impedance Analysis Based on Blind Source Separation
Electrical impedance myography (EIM) is a relatively new technique to evaluate neuromuscular diseases and muscle fatigue. However, EIM is a biomarker of muscle status, and its sensitivity is influenced by skin and fat. To restore the impedivity of the muscle layer from EIM data, we use the ICA and EASI algorithms to estimate the impedivity of each tissue layer and compare the performance of ICA and EASI methods. The results showed that the EASI algorithm can better restore the muscle impedivity with an accuracy rate of 0.999. This work provides a new idea for the treatment of muscle diseases and muscle fatigue recovery.
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