Burr W
EEG EMG Z Elektroenzephalogr Elektromyogr Verwandte Geb. 1980 Sep;11(3):135-41.
The application of automatic approaches in EEG pattern recognition essentially has two aims: to increase the level of objectivity (by means of quantification) and to decrease the amount of diagnostic work (by means of data reduction). In this study a computerized spike-wave detection method is described, realized on a small laboratory computer (PDP 11/03) and especially designed for the analysis of long term registrations. The program is written in FORTRAN in order to provide a high level of flexibility. The user may easily modify it for his own purposes. Pattern recognition is based on the configuration of maxima and minima. Different degrees of digital filtering is used for spike analysis and wave analysis. The time codes of the events are registered and stored on magnetic disk. In a subsequent computer run the results may graphically be displayed or submitted to further analysis. An example is given testing in which way the results depend on the choice of the parameter set (duration and amplitude of spike and wave). The time course of the frequency of SW-events (or regular SW-sequences, respectively) are compared to the results of conventional analysis.
提高客观性水平(通过量化手段)和减少诊断工作量(通过数据简化手段)。在本研究中,描述了一种计算机化的棘波-慢波检测方法,该方法在一台小型实验室计算机(PDP 11/03)上实现,并且专门设计用于长期记录分析。该程序用FORTRAN编写,以便提供高度的灵活性。用户可以根据自己的目的轻松修改它。模式识别基于最大值和最小值的配置。不同程度的数字滤波用于棘波分析和慢波分析。事件的时间代码被记录并存储在磁盘上。在随后的计算机运行中,结果可以以图形方式显示或提交进行进一步分析。给出了一个示例测试,即结果如何取决于参数集(棘波和慢波的持续时间和幅度)的选择。将棘慢波事件(或分别为规则的棘慢波序列)的频率随时间变化过程与传统分析结果进行比较。