Goetz S M, Li Z, Peterchev A V
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2687-2690. doi: 10.1109/EMBC.2018.8512765.
Motor-evoked potentials (MEP) are one of the most important responses to brain stimulation, such as supra-threshold transcranial magnetic stimulation (TMS) and electrical stimulation. The understanding of the neurophysiology and the determination of the lowest stimulation strength that evokes responses requires the detection of even smallest responses, e.g., from single motor units, but available detection and quantization methods are rather simple and suffer from a large noise floor. The paper introduces a more sophisticated matched-filter detection method that increases the detection sensitivity and shows that activation occurs well below the conventional detection level. In consequence, also conventional threshold definitions, e.g., as 50 μV median response amplitude, turn out to be substantially higher than the point at which first detectable responses occur. The presented method uses a matched-filter approach for improved sensitivity and generates the filter through iterative learning from the presented data. In contrast to conventional peak-to-peak measures, the presented method has a higher signal-to-noise ratio (≥14 dB). For responses that are reliably detected by conventional detection, the new approach is fully compatible and provides the same results but extends the dynamic range below the conventional noise floor. The underlying method is applicable to a wide range of well-timed biosignals and evoked potentials, such as in electroencephalography.
运动诱发电位(MEP)是对脑刺激(如阈上经颅磁刺激(TMS)和电刺激)的最重要反应之一。对神经生理学的理解以及对诱发反应的最低刺激强度的确定需要检测即使是最小的反应,例如来自单个运动单位的反应,但现有的检测和量化方法相当简单,且存在较大的本底噪声。本文介绍了一种更复杂的匹配滤波器检测方法,该方法提高了检测灵敏度,并表明激活发生在远低于传统检测水平的情况下。因此,传统的阈值定义(例如,作为50 μV的中位反应幅度)也被证明远高于首次可检测反应出现的点。所提出的方法使用匹配滤波器方法来提高灵敏度,并通过对所呈现的数据进行迭代学习来生成滤波器。与传统的峰峰值测量相比,所提出的方法具有更高的信噪比(≥14 dB)。对于通过传统检测可靠检测到的反应,新方法完全兼容并提供相同的结果,但扩展了低于传统本底噪声的动态范围。其基础方法适用于广泛的适时生物信号和诱发电位,如脑电图中的信号。