Bigoni Claudia, Cadic-Melchior Andéol, Vassiliadis Pierre, Morishita Takuya, Hummel Friedhelm C
Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), 1202 Geneva, Switzerland.
Defitech Chair of Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), EPFL Valais, Clinique Romande de Réadaptation, 1951 Sion, Switzerland.
J Neural Eng. 2022 Apr 21;19(2). doi: 10.1088/1741-2552/ac636c.
Latencies of motor evoked potentials (MEPs) can provide insights into the motor neuronal pathways activated by transcranial magnetic stimulation. Notwithstanding its clinical relevance, accurate, unbiased methods to automatize latency detection are still missing.We present a novel open-source algorithm suitable for MEP onset/latency detection during resting state that only requires the post-stimulus electromyography signal and exploits the approximation of the first derivative of this signal to find the time point of initial deflection of the MEP.The algorithm has been benchmarked, using intra-class coefficient (ICC) and effect sizes, to manual detection of latencies done by three researchers independently on a dataset comprising almost 6500 MEP trials from healthy participants (= 18) and stroke patients (= 31) acquired during rest. The performance was further compared to currently available automatized methods, some of which created for active contraction protocols. MainThe unstandardized effect size between the human raters and the present method is smaller than the sampling period for both healthy and pathological MEPs. Moreover, the ICC increases when the algorithm is added as a rater.The present algorithm is comparable to human expert decision and outperforms currently available methods. It provides a promising method for automated MEP latency detection under physiological and pathophysiological conditions.
运动诱发电位（MEP）的潜伏期能够为经颅磁刺激所激活的运动神经元通路提供深入见解。尽管其具有临床相关性，但仍缺乏准确、无偏倚的自动检测潜伏期的方法。我们提出了一种适用于静息状态下MEP起始/潜伏期检测的新型开源算法，该算法仅需要刺激后的肌电图信号，并利用该信号一阶导数的近似值来找到MEP初始偏转的时间点。该算法已通过类内系数（ICC）和效应量进行基准测试，与三位研究人员独立对一个数据集进行的潜伏期手动检测进行比较，该数据集包含来自18名健康参与者和31名中风患者在静息状态下采集的近6500次MEP试验。其性能还与目前可用的自动化方法进行了进一步比较，其中一些方法是为主动收缩方案创建的。主要内容：人类评分者与本方法之间的非标准化效应量对于健康和病理性MEP均小于采样周期。此外，当将该算法作为评分者添加时，ICC会增加。本算法与人类专家决策相当，且优于目前可用的方法。它为生理和病理生理条件下的MEP潜伏期自动检测提供了一种有前景的方法。
