期刊
CLINICAL NEUROPHYSIOLOGY
卷 125, 期 5, 页码 930-940出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.clinph.2013.10.051
关键词
Intracranial EEG; Neural mass model; Excitatory and inhibitory interaction; Seizure prediction; Focal epilepsy
资金
- NIH [RO1EB007920, RO1EB006433]
- NSF [CBET-0933067, DGE-1069104]
- Minnesota Partnership for Biotechnology and Medical Genomics
Objectives: The aim of this study is to develop a model based seizure prediction method. Methods: A neural mass model was used to simulate the macro-scale dynamics of intracranial EEG data. The model was composed of pyramidal cells, excitatory and inhibitory interneurons described through state equations. Twelve model's parameters were estimated by fitting the model to the power spectral density of intracranial EEG signals and then integrated based on information obtained by investigating changes in the parameters prior to seizures. Twenty-one patients with medically intractable hippocampal and neocortical focal epilepsy were studied. Results: Tuned to obtain maximum sensitivity, an average sensitivity of 87.07% and 92.6% with an average false prediction rate of 0.2 and 0.15/h were achieved using maximum seizure occurrence periods of 30 and 50 min and a minimum seizure prediction horizon of 10 s, respectively. Under maximum specificity conditions, the system sensitivity decreased to 82.9% and 90.05% and the false prediction rates were reduced to 0.16 and 0.12/h using maximum seizure occurrence periods of 30 and 50 min, respectively. Conclusions: The spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction. Significance: The present findings suggest that the model-based approach may aid prediction of seizures. (C) 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据