AUTOMATIC CLASSIFICATION AND ANALYSIS OF MICRONEUROGRAPHIC SPIKE DATA USING A PC/AT
AUTOMATIC CLASSIFICATION AND ANALYSIS OF MICRONEUROGRAPHIC SPIKE DATA USING A PC/AT
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DOI：
10.1016/0165-0270(90)90155-9
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发表时间：
1990-02-01
影响因子：
3
通讯作者：
HANDWERKER, HO
中科院分区：
文献类型：
--
作者：
FORSTER, C;HANDWERKER, HO
使用带有商业模拟数据接口的标准 PC-AT 设计了一个系统，该系统支持显微神经描记实验，也可用于其他类型的细胞外尖峰记录。信号以 25 kHz 在线采样，如果信号超过特定阈值，则会检测到尖峰。峰值显示在屏幕上并存储在磁盘上。第二种在线模式记录被检查单元对电刺激的响应，用于识别纤维类型并测试随后的尖峰分类。使用模板匹配算法对尖峰进行离线分类，该算法具有无监督学习和区分阶段。结果显示在时频图中，并且可以通过对电刺激的响应进行检查。来自肌电图和其他电场的伪影被可靠地分类出来。在包含多个单元的录音中，它们的尖峰以较低的错误率被识别。
Using a standard PC-AT with a commercial analog data interface a system was designed which supports microneurographic experiments and which may also be used for other types of extracellular spike recordings. The signal is sampled on-line at 25 kHz and a spike is detected if the signal passes a certain threshold. The spikes are displayed on the screen and stored on disk. A second on-line mode records the responses of the examined unit to electrical stimulations, which are used to identify the type of fibre and to test the subsequent spike classification. The spikes are classified off-line using a template matching algorithm, which has unsupervised learning and discrimination phases. The results are displayed in a time-frequency plot and may be checked with the responses to electrical stimulations. Artifacts from EMG and other electrical fields are reliably sorted out. In recordings, which include more than one unit, their spikes are discriminated with a low error rate.