Combined process automation for large-scale EEG analysis

被引：6

作者：

Sfondouris, John L. ^{[1
]}

Quebedeaux, Tabitha M. ^{[1
]}

Holdgraf, Chris ^{[2
]}

Musto, Alberto E. ^{[1
]}

机构：

[1] Louisiana State Univ, Hlth Sci Ctr, Neurosci Ctr Excellence, New Orleans, LA 70112 USA

[2] Univ Calif Berkeley, Grad Sch, Helen Wills Neurosci Inst, Berkeley, CA 94720 USA

来源：

COMPUTERS IN BIOLOGY AND MEDICINE | 2012年 / 42卷 / 01期

关键词：

Epilepsy; EEG; Stimulation; After-discharge; Automation; Algorithm; ANIMAL-MODELS; RATS; RECORDINGS; SEIZURES;

D O I：

10.1016/j.compbiomed.2011.10.017

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：129 / 134

页数：6

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