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两种用于低阈值运动单位非融合强直收缩中动作电位序列估计方法的优化与比较

Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units.

作者信息

Rohlén Robin, Antfolk Christian, Grönlund Christer

机构信息

Department of Biomedical Engineering, Lund University, Lund, Sweden; Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden.

Department of Biomedical Engineering, Lund University, Lund, Sweden.

出版信息

J Electromyogr Kinesiol. 2022 Dec;67:102714. doi: 10.1016/j.jelekin.2022.102714. Epub 2022 Oct 2.

DOI:10.1016/j.jelekin.2022.102714
PMID:36209700
Abstract

BACKGROUND

Recent findings have shown that imaging voluntarily activated motor units (MUs) by decomposing ultrasound-based displacement images provides estimates of unfused tetanic signals evoked by spinal motoneurons' neural discharges (spikes). Two methods have been suggested to estimate its spike trains: band-pass filter (BPM) and Haar wavelet transform (HWM). However, the methods' optimal parameters and which method performs the best are unknown. This study will answer these questions.

METHOD

HWM and BPM were optimized using simulations. Their performance was evaluated based on simulations and 21 experimental datasets, considering their rate of agreement, spike offset, and spike offset variability to the simulated or experimental spikes.

RESULTS

A range of parameter sets that resulted in the highest possible agreement with simulated spikes was provided. Both methods highly agreed with simulated and experimental spikes, but HWM was a better spike estimation method than BPM because it had a higher agreement, less bias, and less variation (p < 0.001).

CONCLUSIONS

The optimized HWM will be an important contributor to further developing the identification and analysis of MUs using imaging, providing indirect access to the neural drive of the spinal cord to the muscle by the unfused tetanic signals.

摘要

背景

最近的研究结果表明,通过分解基于超声的位移图像来对自主激活的运动单位(MU)进行成像,可以估计脊髓运动神经元神经放电(尖峰)诱发的不完全强直信号。已提出两种方法来估计其尖峰序列:带通滤波器(BPM)和哈尔小波变换(HWM)。然而,这些方法的最佳参数以及哪种方法表现最佳尚不清楚。本研究将回答这些问题。

方法

使用模拟对HWM和BPM进行优化。基于模拟和21个实验数据集对它们的性能进行评估,考虑它们与模拟或实验尖峰的一致性率、尖峰偏移以及尖峰偏移变异性。

结果

提供了一系列与模拟尖峰达成最高可能一致性的参数集。两种方法与模拟和实验尖峰高度一致,但HWM是比BPM更好的尖峰估计方法,因为它具有更高的一致性、更小的偏差和更小的变异性(p < 0.001)。

结论

优化后的HWM将为进一步发展利用成像技术识别和分析运动单位做出重要贡献,通过不完全强直信号间接获取脊髓对肌肉的神经驱动。

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引用本文的文献

1
Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation.利用卷积盲源分离从低阈值运动单位的未融合强直信号中估计神经尖峰序列。
Biomed Eng Online. 2023 Feb 7;22(1):10. doi: 10.1186/s12938-023-01076-0.