SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis
SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis
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DOI：
10.1016/j.clinph.2019.03.032
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发表时间：
2019-07-01
影响因子：
4.7
通讯作者：
Shaw, C.
中科院分区：
文献类型：
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作者：
Bashford, J.;Wickham, A.;Shaw, C.
目的：肌束震颤是肌萎缩侧索硬化（ALS）的一个临床特征。与同心针肌电图相比，高密度表面肌电图（HDSEMG）是非侵入性的，并且在更长的持续时间内记录更大肌肉体积的肌束震颤电位（FP）。要检测和HDSEMG产生的巨大的数据集，我们开发了一个自动化的分析tool.Methods：6 ALS患者和两个对照患者（一个良性肌束震颤综合征和一个多灶性运动神经病）进行了30分钟的HDSEMG从二头肌和腓肠肌每月。在MATLAB中，我们开发了一种新颖的创新方法来识别波动噪声水平中的FP。100次重复的5倍交叉验证估计了模型的预测能力。结果：通过应用这种方法，我们从80分钟的记录中识别出5,318个FP，灵敏度为83.6%（+/- 0.2 SEM），特异性为91.6%（+/-0.1 SEM），分类准确率为87.9%（+/- 0.1 SEM）。振幅排除阈值（100 μ V）删除了过度嘈杂的数据，而不影响灵敏度。由此产生的自动FP计数没有显着不同的手动计数（p = 0.394）。结论：我们已经设计和内部验证了一种自动化的方法，以准确地识别FP从HDSEMG，一种技术，我们命名为表面电位量化引擎（SPiQE）。意义：ALS中肌束震颤的纵向量化可以提供对运动神经元健康的独特见解。(C)2019国际临床神经生理学联合会。由爱思唯尔公司出版
Objectives: Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool.Methods: Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model's predictive ability.Results: By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/- 0.2 SEM), specificity of 91.6% (+/- 0.1 SEM) and classification accuracy of 87.9% (+/- 0.1 SEM). An amplitude exclusion threshold (100 mu V) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394).Conclusion: We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). Significance: Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health. (C) 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.