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使用具有外部输入的自回归(ARX)模型进行单扫描分析。

Single-sweep analysis using an autoregressive with exogenous input (ARX) model.

作者信息

Magni R, Giunti S, Bianchi A, Reni G, Bandello F, Durante A, Cerutti S, Brancato R

机构信息

Department of Ophthalmology and Visual Sciences, Scientific Institute Hospital San Raffaele, University of Milan, Italy.

出版信息

Doc Ophthalmol. 1994;86(1):95-104. doi: 10.1007/BF01224631.

Abstract

Single-sweep visual evoked potential analysis would be useful in clinical electrophysiology practice because it would make possible the evaluation of transient phenomena, but recording single-sweep visual evoked potentials is difficult because of the low signal-noise ratio. To increase this ratio we used a filter based on an autoregressive with exogenous input model. We studied a group of 12 diabetic patients matched with a control group of 14 normal subjects. The model, in most cases, allowed us to extrapolate the P100 component from each single sweep of visual evoked potential. The visual evoked potential values obtained by means of averaging were not significantly different in the groups studied, but single-sweep analysis showed different distribution of the P100 component amplitude. The preliminary results of our study evidenced differences in the amplitude and latency distribution of normal and diabetic subjects, thus confirming the power of this new technique and its ability to obtain some information that is masked by the averaging method.

摘要

单次扫描视觉诱发电位分析在临床电生理实践中会很有用,因为它能对瞬态现象进行评估,但由于信噪比低,记录单次扫描视觉诱发电位很困难。为了提高这个比率,我们使用了一种基于带有外生输入模型的自回归滤波器。我们研究了一组12名糖尿病患者,并与一组14名正常受试者的对照组进行匹配。在大多数情况下,该模型使我们能够从视觉诱发电位的每次单次扫描中推断出P100成分。通过平均获得的视觉诱发电位值在所研究的组中没有显著差异,但单次扫描分析显示P100成分振幅的分布不同。我们研究的初步结果证明了正常人和糖尿病患者在振幅和潜伏期分布上的差异,从而证实了这项新技术的效力及其获取一些被平均方法掩盖的信息的能力。

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