Use of spatiotemporal templates for pathway discrimination in peripheral nerve recordings: a simulation study

Use of spatiotemporal templates for pathway discrimination in peripheral nerve recordings: a simulation study
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DOI:
10.1088/1741-2552/14/1/016013
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发表时间:
2017-02-01
影响因子:
4
通讯作者:
Zariffa, Jose
Zariffa, Jose
中科院分区:
工程技术2区
文献类型:
--
作者:
Koh, Ryan G. L.;Nachman, Adrian I.;Zariffa, Jose

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目标。从周围神经系统提取信息可以在神经假体应用中提供控制信号。然而,选择性地记录周围神经内不同路径的能力是有限的。我们通过多点接触神经袖带电极的测量,研究了外周神经路径辨别的空间和时间信息的整合。接近。为不同的感兴趣神经通路建立时空模板,并用于获得这些通路中每一条的定制匹配滤波器。使用不同测试案例中通过神经传播的复合动作电位的模拟测量来评估分类准确性、遗漏峰电位的百分比以及重建神经通路的原始放电率的能力。主要结果。对于我们的算法、贝叶斯空间滤波器和速度选择记录,在所有测试情况下,原始放电率和估计放电率之间的平均皮尔森相关系数分别为0.832+/-0.161、0.421+/-0.145、0.481+/-0.340。意义重大。该方法表明,时空模板能够提供比现有算法更健壮的棘波检测和可靠的路径区分。
Objective. Extraction of information from the peripheral nervous system can provide control signals in neuroprosthetic applications. However, the ability to selectively record from different pathways within peripheral nerves is limited. We investigated the integration of spatial and temporal information for pathway discrimination in peripheral nerves using measurements from a multi-contact nerve cuff electrode. Approach. Spatiotemporal templates were established for different neural pathways of interest, and used to obtain tailored matched filters for each of these pathways. Simulated measurements of compound action potentials propagating through the nerve in different test cases were used to evaluate classification accuracy, percentage of missed spikes, and ability to reconstruct the original firing rates of the neural pathways. Main results. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates over all tests cases was found to be 0.832 +/- 0.161, 0.421 +/- 0.145, 0.481 +/- 0.340 for our algorithm, Bayesian spatial filters, and velocity selective recordings respectively. Significance. The proposed method shows that the spatiotemporal templates were able to provide more robust spike detection and reliable pathway discrimination than these existing algorithms.