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
收藏
页码：129 / 134
页数：6
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