FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAPHIC SIGNALS INTRACEREBRALLY RECORDED DURING 35 EPILEPTIC SEIZURES - EVALUATION OF A NEW METHOD FOR SYNOPTIC VISUALIZATION OF ICTAL EVENTS
FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAPHIC SIGNALS INTRACEREBRALLY RECORDED DURING 35 EPILEPTIC SEIZURES - EVALUATION OF A NEW METHOD FOR SYNOPTIC VISUALIZATION OF ICTAL EVENTS
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DOI：
10.1016/0013-4694(94)00181-2
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发表时间：
1994-11-01
期刊：
影响因子：
--
通讯作者：
BINNIE, CD
中科院分区：
文献类型：
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作者：
BULLMORE, ET;BRAMMER, MJ;BINNIE, CD
传统的脑电图（EEG）产生大容量的脑电活动显示，特别是在评估长时间的颅内立体脑电图（SEEG）记录时产生了问题。报告了一种分形分析方法，该方法描述了100个SEEG数据点的分形维数（1 < FD < 2）的单一估计；中央处理单元的时间成本约为2分钟/Mbyte的输入信号（使用Sun SPARCstation LX）。该方法应用于7例患者中35例癫痫发作期间记录的SEEG信号的量化和概要可视化，评估了该方法的诊断敏感性。研究发现，该方法一致地根据多个通道中FD的快速相对增加来定义发作。临床上严重的癫痫发作的特点是FD比临床上不太严重的事件更强烈和更广泛的体征变化。对于所有7例患者和75%的癫痫发作个体，解剖定义的癫痫发作区“分形诊断”与检查相同数据的传统脑电图显示独立确定的癫痫发作区密切吻合。我们得出的结论是，该方法是一种计算上可行的方法，可以实现SEEG数据量的大幅减少，而不会导致主信号中诊断重要信息的不必要损失。
Traditional electroencephalography (EEG) produces a large volume display of brain electrical activity, which creates problems particularly in assessment of long periods of intracranial, stereoelectroencephalographic (SEEG) recording. A method for fractal analysis that describes 100 SEEG data points in terms of a single estimate of fractal dimension (1 < FD < 2) is reported; the central processing unit time costs amount to approximately 2 min/Mbyte of input signal (using a Sun SPARCstation LX). The diagnostic sensitivity of this method, applied to quantification and synoptic visualisation of SEEG signals recorded during 35 epileptic seizures in 7 patients, is evaluated. II is found that the method consistently defines ictal onset in terms of rapid relative increase in FD across several channels. Clinically severe seizures are characterised by more intense and generalised ictal changes in FD than clinically less severe events. For all 7 patients, and for 75% of individual seizures, ''fractal diagnoses'' of anatomically defined ictal onset zone coincided closely with ictal onset zone independently determined by inspection of traditional EEG displays of the same data. We conclude that the method is a computationally feasible way to achieve substantial reduction in the volume of SEEG data without undue loss of diagnostically important information in the primary signal.