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Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials

机译:波形相似性分析:用于检测和量化嘈杂诱发的复合动作电位的简单模板比较方法

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摘要

Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies.
机译:由于分散在各种潜伏期和不良信噪比的事件的典型复杂响应,对再生神经引起的诱发反应的实验电生理评估具有挑战性。我们的目标是使用简单的互相关模板比较方法自动检测复合动作潜在事件并导出其潜伏期和幅度。为此,我们开发了一种称为波形相似性分析的算法。为了测试该算法,通过刺激腓肠神经和坐骨神经在体内产生了具有挑战性的信号,同时分别记录了从坐骨神经横断中恢复的动物在坐骨神经和胫骨前肌的诱发电位。我们算法的模板是根据完整方面引起的响应生成的。我们还模拟了噪声信号,并使用不完善的模板检查了波形相似度分析算法的输出。使用波形相似度分析对信号进行检测和量化,然后将其与由受过训练的观察者对同一信号进行事件检测,潜伏期和幅度测量相比较,这一过程称为“训练眼图分析”。波形相似性分析算法可以成功地检测和量化来自完整或再生神经的神经和肌肉复合动作电位的简单或复杂响应。错误地指定模板的效果优于训练眼图分析(用于预测信号幅度),但是对于所检查的模拟信号却产生了一致的等待时间误差。与受过训练的眼睛相比,“波形相似性分析”是自动的,客观的,不依赖观察者来识别和/或测量峰值,即使信噪比很差,也可以检测到小的聚类事件。波形相似性分析提供了一种简单,可靠和方便的方法来量化复杂波形的等待时间和幅度,因此可作为研究神经再生研究中诱发的复合动作电位的有用工具。

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