Automated detection of latency tracks in microneurography recordings using track correlation
Automated detection of latency tracks in microneurography recordings using track correlation
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DOI：
10.1016/j.jneumeth.2016.01.004
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发表时间：
2016-03-15
影响因子：
3
通讯作者：
Namer, Barbara
中科院分区：
文献类型：
--
作者：
Turnquist, Brian;RichardWebster, Brandon;Namer, Barbara
背景：显微神经造影术中的标记技术使用刺激诱导的神经传导速度变化来表征人类C纤维。传导速度的变化表现为周期性电刺激和所产生的AP之间的时间延迟的变化。当连续记录的扫描以“瀑布”格式垂直显示时，与刺激相关的AP形成可见的垂直轨迹。自动检测这些潜伏期的轨道是困难的，有时记录，自发神经放电不相关的刺激，交叉或密切平行的tracks.New方法的信噪比差，和多单元记录：我们开发了一种自动跟踪检测技术的基础上的局部线性化的潜伏期轨道的刺激相关的AP。这种技术增强了潜伏期的轨道，消除瞬态噪声尖峰和自发的神经活动不相关的刺激，并自动检测潜伏期的轨道在recording.Results连续扫描：我们评估了我们的方法上microneurography记录显示不同的信号质量，自发放电，和多个轨道运行密切平行和交叉。该方法表现出出色的检测潜伏期的轨道在我们所有的recording.Comparison与现有的方法（S）：我们比较我们的方法，以常用的轨道检测方法的Hammarberg实现在Dreyer program.Conclusions：我们的方法是一个强大的手段，自动检测潜伏期的轨道在典型的microneurography记录。(C)© 2016 Elsevier B.V.版权所有。
Background: The marking technique in microneurography uses stimulus-induced changes in neural conduction velocity to characterize human C-fibers. Changes in conduction velocity are manifested as variations in the temporal latency between periodic electrical stimuli and the resulting APs. When successive recorded sweeps are displayed vertically in a "waterfall" format, APs correlated with the stimulus form visible vertical tracks. Automated detection of these latency tracks is made difficult by sometimes poor signal-to-noise ratio in recordings, spontaneous neural firings uncorrelated with the stimuli, and multi-unit recordings with crossing or closely parallel tracks.New method: We developed an automated track-detection technique based on a local linearization of the latency tracks of stimulus-correlated APs. This technique enhances latency tracks, eliminates transient noise spikes and spontaneous neural activity not correlated with the stimulus, and automatically detects latency tracks across successive sweeps in a recording.Results: We evaluated our method on microneurography recordings showing varying signal quality, spontaneous firing, and multiple tracks that run closely parallel and cross. The method showed excellent detection of latency tracks in all of our recordings.Comparison with existing method(s): We compare our method to the commonly used track detection method of Hammarberg as implemented in the Dreyer program.Conclusions: Our method is a robust means of automatically detecting latency tracks in typical microneurography recordings. (C) 2016 Elsevier B.V. All rights reserved.