Stewart C M, Newlands S D, Perachio A A
Department of Otolaryngology, University of Texas Medical Branch, Galveston, TX 77555-1063, USA.
Comput Methods Programs Biomed. 2004 Dec;76(3):239-51. doi: 10.1016/j.cmpb.2004.07.001.
Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.
从细胞外记录中快速准确地识别单个神经元是分析和解释电生理记录的基本过程。我们提出了一种算法，用于对细胞外记录过程中的动作电位进行检测、表征、识别和分析。该程序完全用LabVIEW（美国国家仪器公司）编写，无需外部硬件设备，也不需要关于动作电位形状的先验信息。通过扫描数字记录中超过用户可调节触发电压的信号来检测波形事件。对检测到的事件进行表征，以确定每个事件的九个不同的时间和电压水平。这些波形特征的各种代数组合被用作事件二维笛卡尔图的轴选择。用户选择能生成不同簇的轴选择。通过手动生成任意形状的边界，可以将多个簇定义为动作电位。通过对叠加波形的目视检查来验证被定义为动作电位的事件。通过选择任何记录通道与分箱单元数据的连续和平均周期直方图进行比较，可以识别刺激-反应关系。该算法包括特征分析和采集的新方面，包括与其他通道相比更高的电生理数据采集速率。该程序证实，使用从高速数字记录中导出的波形特征的代数组合，可以高速、高效地辨别电生理数据。
