Mishra R B, Tripathi A N
Electrical Engg. Deptt. IT-BHU, Varanasi, India.
Int J Clin Monit Comput. 1991;8(1):1-11. doi: 10.1007/BF02916086.
Parametric and Non-parametric methods have been developed for the detection and interpretation of EEG data for normal and abnormal patients. These methods have been implemented on mainframe computers or dedicated microcomputers. The heuristic methods are suitable for implement on dedicated microprocessor based system as they involve less degree of computation in comparison to the parametric methods. In this work a microprocessor based system has been developed and heuristic pattern recognition technique has been applied, which is based on the measurement of amplitude, duration slopes etc. for the detection of spike and sharp waves as well as the different frequency band of background activity. The computed values of amplitude and durations are shown on the graphs from which the different symptoms based on EEG are determined.
已经开发出参数化和非参数化方法来检测和解释正常及异常患者的脑电图(EEG)数据。这些方法已在大型计算机或专用微型计算机上实现。启发式方法适合在基于专用微处理器的系统上实现,因为与参数化方法相比,它们的计算量较小。在这项工作中,开发了一种基于微处理器的系统,并应用了启发式模式识别技术,该技术基于对幅度、持续时间斜率等的测量来检测尖峰和锐波以及背景活动的不同频段。幅度和持续时间的计算值显示在图表上,据此确定基于脑电图的不同症状。