Seizure prediction using scalp electroencephalogram

被引:16
|
作者
Drury, I
Smith, B
Li, DZ
Savit, R
机构
[1] Diagnost Neurodynam LLC, Ann Arbor, MI 48104 USA
[2] Henry Ford Hlth Syst, Dept Neurol, Detroit, MI 48202 USA
[3] Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA
[4] Univ Michigan, Div Biophys Res, Ann Arbor, MI 48109 USA
关键词
mesial temporal lobe epilepsy; nonlinear dynamics; seizure prediction; marginal predictability;
D O I
10.1016/S0014-4886(03)00354-6
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Using a measure of nonlinear dynamical changes we term marginal predictability, we report evidence of robust changes in this parameter on scalp EEG in a cohort of patients with medically refractory mesiobasal temporal lobe epilepsy (MBTLE). In the baseline (interictal) state there are distinct differences in this nonlinear measure between epileptic and neurologically normal subjects. At baseline, in patients with MBTLE there are differences in these measures between electrodes adjacent to the ictal onset zone and more remotely placed electrodes. The character of these differences evolves over a period of approximately 30 min before a seizure. We discuss and integrate our findings with two emerging concepts in epileptology, first, the concept of a preictal or transition phase rather than an abrupt movement from interictal to ictal activity, and second, the notion of an epileptic neural network with changes in areas of brain remote from what has traditionally been considered the ictal onset zone influencing "ictogenesis." (C) 2003 Elsevier Inc. All rights reserved.
引用
收藏
页码:S9 / S18
页数:10
相关论文
共 50 条
  • [21] Seizure Prediction using EEG Segmentation Change Points
    Ghasemi, Nasim
    Mosavi, Mohammad Reza
    2017 3RD IRANIAN CONFERENCE ON SIGNAL PROCESSING AND INTELLIGENT SYSTEMS (ICSPIS), 2017, : 17 - 22
  • [22] SEIZURE PREDICTION USING EEG CHANNEL SELECTION METHOD
    Wang, Xiaoshuang
    Karkkainen, Tommi
    Cong, Fengyu
    2022 IEEE 32ND INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2022,
  • [23] Automated seizure prediction
    Acharya, U. Rajendra
    Hagiwara, Yuki
    Adeli, Hojjat
    EPILEPSY & BEHAVIOR, 2018, 88 : 251 - 261
  • [24] Seizure prediction and intervention
    Meisel, Christian
    Loddenkemper, Tobias
    NEUROPHARMACOLOGY, 2020, 172
  • [25] Seizure prediction: Methods
    Carney, Paul R.
    Myers, Stephen
    Geyer, James D.
    EPILEPSY & BEHAVIOR, 2011, 22 : S94 - S101
  • [26] Epileptic Seizure Prediction Using Attention Augmented Convolutional Network
    Liu, Dongsheng
    Dong, Xingchen
    Bian, Dong
    Zhou, Weidong
    INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2023, 33 (11)
  • [27] On the Validity of Using Probing Stimuli for Seizure Prediction in the Epileptor Model
    Carvalho, Vinicius R.
    Moraes, Marcio F. D.
    Mendes, Eduardo M. A. M.
    COMPUTATIONAL NEUROSCIENCE, 2019, 1068 : 269 - 281
  • [28] Seizure Prediction in EEG Signals Using STFT and Domain Adaptation
    Peng, Peizhen
    Song, Yang
    Yang, Lu
    Wei, Haikun
    FRONTIERS IN NEUROSCIENCE, 2022, 15
  • [29] Seizure Prediction in Epileptic Patients Using EEG and Anomaly Detection
    Mirzaei, Erfan
    Shamsollahi, Mohammad Bagher
    2022 29TH NATIONAL AND 7TH INTERNATIONAL IRANIAN CONFERENCE ON BIOMEDICAL ENGINEERING, ICBME, 2022, : 114 - 118
  • [30] Epileptic Seizure Prediction from EEG Signals Using DenseNet
    Jana, Ranjan
    Bhattacharyya, Siddhartha
    Das, Swagatam
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 604 - 609