Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states

被引：0

作者：

Itaf Ben Slimen ^{[1
]}

Larbi Boubchir ^{[2
]}

Hassene Seddik ^{[1
]}

机构：

[1] Centre de Recherche et de Production Research Lab,Ecole Nationale Superieure des Ingenieurs de Tunis,University of Tunis

[2] Laboratoire d'Informatique Avancee de Saint-Denis Research Lab,University of Paris

来源：

The Journal of Biomedical Research | 2020年 / 34卷 / 03期

关键词：

electroencephalogram; epilepsy; seizure prediction; spikes detection;

D O I：

暂无

中图分类号：

R742.1 [癫痫];

学科分类号：

1002 ;

摘要：

Epileptic seizures are known for their unpredictable nature.However,recent research provides that the transition to seizure event is not random but the result of evidence accumulations.Therefore,a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients.Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes,spikes,and the amplitude.In this study,spike rate is used as the indicator to anticipate seizures in electroencephalogram(EEG) signal.Spikes detection step is used in EEG signal during interictal,preictal,and ictal periods followed by a mean filter to smooth the spike number.The maximum spike rate in interictal periods is used as an indicator to predict seizures.When the spike number in the preictal period exceeds the threshold,an alarm is triggered.Using the CHB-MIT database,the proposed approach has ensured92% accuracy in seizure prediction for all patients.

引用

页码：162 / 169

页数：8

共 25 条

[1]

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals[J] . Κostas Μ. Tsiouris,Vasileios C. Pezoulas,Michalis Zervakis,Spiros Konitsiotis,Dimitrios D. Koutsouris,Dimitrios I. Fotiadis.Computers in Biology and Medicine . 2018

[2]

Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy[J] . Honghui Zhang,Jianzhong Su,Qingyun Wang,Yueming Liu,Levi Good,Juan M. Pascual.Communications in Nonlinear Science and Numerical . 2018

[3]

Chaos feature study in fractional Fourier domain for preictal prediction of epileptic seizure[J] . Keling Fei,Wei Wang,Qiaoli Yang,Shusen Tang.Neurocomputing . 2017

[4]

Predicting epileptic seizures from scalp EEG based on attractor state analysis[J] . Hyunho Chu,Chun Kee Chung,Woorim Jeong,Kwang-Hyun Cho.Computer Methods and Programs in Biomedicine . 2017

[5]

Towards accurate prediction of epileptic seizures: A review[J] . Elie Bou Assi,Dang K. Nguyen,Sandy Rihana,Mohamad Sawan.Biomedical Signal Processing and Control . 2017

[6]

On the proper selection of preictal period for seizure prediction[J] . Mojtaba Bandarabadi,Jalil Rasekhi,César A. Teixeira,Mohammad R. Karami,António Dourado.Epilepsy & Behavior . 2015

[7] A low computation cost method for seizure prediction [J].

Zhang, Yanli ;

Zhou, Weidong ;

Yuan, Qi ;

Wu, Qi .

EPILEPSY RESEARCH, 2014, 108 (08) :1357-1366

[8] Construction of rules for seizure prediction based on approximate entropy [J].

Zhang, Zhen ;

Chen, Ziyi ;

Zhou, Yi ;

Du, Shouhong ;

Zhang, Yang ;

Mei, Tian ;

Tian, Xianghua .

CLINICAL NEUROPHYSIOLOGY, 2014, 125 (10) :1959-1966

[9]

Epileptic Seizure Prediction Using Relative Spectral Power Features[J] . Mojtaba Bandarabadi,César A. Teixeira,Jalil Rasekhi,António Dourado.Clinical Neurophysiology . 2014

[10]

Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients[J] . César Teixeira,Bruno Direito,Mojtaba Bandarabadi,Michel le Van Quyen,Mario Valderrama,Bjoern Schelter,Andreas Schultz-Bonhage,Vincent Navarro,Francisco Sales,Antonio Dourado.Computer Methods and Programs in Biomedicine . 2014

← 1 2 3 →