Automatic spike detection based on adaptive template matching for extracellular neural recordings
Automatic spike detection based on adaptive template matching for extracellular neural recordings
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DOI：
10.1016/j.jneumeth.2007.05.033
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发表时间：
2007-09-30
影响因子：
3
通讯作者：
McNames, James
中科院分区：
文献类型：
--
作者：
Kim, Sunghan;McNames, James
细胞外神经活动的记录被用于许多临床应用和科学研究。在大多数情况下，这些信号被分析为点过程，并且需要尖峰检测算法来估计动作电位发生的时间。来自高密度微电极阵列（MEA）和低阻抗微电极的记录通常具有低信噪比（SNR < 10）并且包含来自多个神经元的动作电位。我们描述了一种新的检测算法的基础上模板匹配，只需要用户指定的最小和最大发射率的神经元。该算法迭代地估计最突出的动作电位的形态。在估计SNR = 3的记录中，它能够实现> 90%的灵敏度，假阳性率< 5 Hz，并且在估计SNR > 2.5的记录中，它的性能优于最佳阈值检测器。(c)2007 Elsevier B.V.保留所有权利。
Recordings of extracellular neural activity are used in many clinical applications and scientific studies. In most cases, these signals are analyzed as a point process, and a spike detection algorithm is required to estimate the times at which action potentials occurred. Recordings from high-density microelectrode arrays (MEAs) and low-impedance ruicroelectrodes often have a low signal-to-noise ratio (SNR < 10) and contain action potentials from more than one neuron. We describe a new detection algorithm based on template matching that only requires the user to specify the minimum and maximum firing rates of the neurons. The algorithm iteratively estimates the morphology of the most prominent action potentials. It is able to achieve a sensitivity of > 90% with a false positive rate of < 5 Hz in recordings with an estimated SNR = 3, and it performs better than an optimal threshold detector in recordings with an estimated SNR > 2.5. (c) 2007 Elsevier B.V. All rights reserved.