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癫痫发作可预测性的一种新方法:卡尔曼-洛夫特维度。

A new approach towards predictability of epileptic seizures: KLT dimension.

作者信息

Venugopal Rajeshkumar, Narayanan K, Prasad Awadhesh, Spanias A, Sackellares J C, Iasemidis L D

机构信息

Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USA.

出版信息

Biomed Sci Instrum. 2003;39:123-8.

Abstract

This paper proposes a measure of complexity of the epileptic electroencephalogram (EEG) based on the dimensionality of the Karhunen-Loeve Transform (KLT) in the time domain. We estimate the KLT dimensionality by assuming the same observation noise level in the EEG during the interictal period (between the seizures) as the one during an epileptic seizure (ictal period). Utilizing an optimality criterion based on the T-index [1] and the predictability time, derived from the created KLT dimensionality profiles, we show that 10 out of 15 seizures in one patient with temporal lobe epilepsy were predictable with an average predictability time of about 36 minutes.

摘要

本文提出了一种基于时域卡尔胡宁-洛伊夫变换(KLT)维度的癫痫脑电图(EEG)复杂性度量方法。我们通过假设发作间期(癫痫发作之间)EEG中的观测噪声水平与癫痫发作期(发作期)相同来估计KLT维度。利用基于T指数[1]和从创建的KLT维度分布得出的可预测时间的最优性标准,我们表明,一名颞叶癫痫患者的15次发作中有10次是可预测的,平均可预测时间约为36分钟。

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