Time-varying statistical dimension analysis with application to newborn scalp EEG seizure signals

被引：9

作者：

Celka, P

Colditz, P

机构：

[1] Queensland Univ Technol, Signal Proc Res Ctr, Brisbane, Qld 4001, Australia

[2] Univ Queensland, Royal Hosp Women, Perinatal Res Ctr, Brisbane, Qld 4029, Australia

来源：

MEDICAL ENGINEERING & PHYSICS | 2002年 / 24卷 / 01期

关键词：

adaptive principal component analysis; model selection; time series analysis; complexity measure; EEG seizure; newborn;

D O I：

10.1016/S1350-4533(01)00119-9

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

A new approach to the analysis of nonstationary possibly nonlinear time series is presented. It is based on an adaptive autocovariance eigenspectrum. computation known as APEX together with the Rissanen's Minimum Description Length criterion for the selection of the most relevant eigenvalues. A new concept of time-varying instantaneous statistical dimension is introduced. The motivation for this new approach is the analysis of newborn electroencephalogram for which nonstationarity is an inherent property. The proposed algorithm and new dimension are first assessed on synthetic data. Then, newborn scalp EEG data are analyzed using the proposed scheme. Transitions between different brain states are shown to occur on a baby having electrical and clinical seizures. (C) 2002 IPEM. Published by Elsevier Science Ltd. All rights reserved.

引用

页码：1 / 8

页数：8

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