诱发电位的在线信噪比管理——评估与解释反应质量

文献信息

中文摘要

背景： 诱发电位（EPs）是一种优雅、无创且可靠的技术，用于评估神经通路的功能完整性。然而，它们常常受限于持续区分信号与噪声的难度和所需时间。方法：我们最近提出了一种基于频域诱发电位-背景比（EBR）的新技术，利用最先进的模数转换实现快速数据采集，并提供关于实际信号质量的在线反馈。此外，我们开发了一种新的基于模型的信噪比管理概念，允许抑制生物和技术干扰（吞咽、刺激伪迹、电极极化以及电源线电位），并在线评估诱发电位的信噪比（SNR）。在这项工作中，我们通过在十名健康志愿者中研究正中神经刺激后的皮层诱发电位和高频振荡（HFOs），实验证实了这一概念。结果：数学模型和人体数据均表明，频谱目标频带EBR决定了信噪比随扫描次数增加而改善的进程。对于皮层诱发电位，在所有参与者中，超过90次平均后信噪比超过10 dB。信噪比>20 dB表明信号质量优异且可重复性好。对于高频振荡，信噪比向较低值偏移了12 dB，显示出明显的个体差异，但高频振荡频带背景活动的变异较小（25%和75%百分位数之间为1.9 vs. 7.6 dB）。因此，与实际信号提取相比，个体高频振荡反应比背景活动更为重要。在高频振荡振幅高的受试者中，可重复性在少于1000次扫描时得到证实。结论：本研究证实，个体EBR是决定信噪比的主要因素。背景噪声可以降低到可忽略的水平。背景活动的在线评估将允许对原始信号质量进行最准确的逐时刻可视化。这将有助于终止数据采集，并且可能基于量化的信号质量而非预定义的扫描次数。

英文摘要

(1) Background: Evoked potentials (EPs) are an elegant, non-invasive, and reliable technique for assessing the functional integrity of neural pathways. They are, however, often limited by the difficulty and time needed to consistently distinguish signal from noise. (2) Methods: We have recently proposed a novel technique based on spectral domain evoked-to-background ratio (EBR) that enables fast data acquisition with online feedback about actual signal quality utilizing state of the art analog-to-digital conversion. Furthermore, we have developed a novel model-based signal-to-noise management concept allowing for suppression of biological and technical interference (swallowing, stimulation artifacts, electropolarization, and powerline potentials) and for online assessment of signal-to-noise ratio (SNR) for EPs. In this work, we experimentally confirmed this concept in ten healthy volunteers by investigating cortical EPs and high-frequency oscillations (HFOs) following median nerve stimulation. (3) Results: Both mathematical model and human data demonstrate that spectral target-band EBR governs the progress in SNR with increasing sweep count. For cortical EPs, SNR exceeded 10 dB beyond 90 averages in all participants. An SNR > 20 dB documented excellent signal quality and reproducibility. For HFOs, the SNR shifted to lower values by 12 dB, displaying pronounced individual variation, however, with smaller variation of HFO-band background activity (1.9 vs. 7.6 dB between the 25% and 75% percentile). Thus, individual HFO responses are more important for actual signal extraction compared to background activity. In subjects displaying a high HFO amplitude, reproducibility was confirmed for less than 1000 sweeps. (4) Conclusions: The present investigations confirm that individual EBR is the major factor defining SNR. Background noise can be reduced to a negligible level. Online assessment of background activity will allow for the most accurate moment-to-moment visualization of raw signal quality. This will facilitate termination of the data acquisition and may be based on quantified signal quality rather than predefined sweep count.