EEG Classification of Epilepsy Based on Edge-center Network Feature Extraction

doi:10.3969/j.issn.1006-2475.2024.05.005

Abstract

Abstract: Abstract: Epilepsy is one of the most common neurological diseases， and accurate seizure detection is crucial for treatment. In order to improve the accuracy of automatic identification and diagnosis of epileptic EEG signals， we design an edge-centered method to construct complex networks. Firstly， the Z-score value of the series was calculated， and the edge time series was constructed by dot product operation. Secondly， the Pearson correlation coefficient was calculated to construct the edge matrix. Finally， the feature parameters are obtained through network analysis， and three classifiers including SVM， K-NN and LR are selected for comparative classification research. The experimental results show that the classification method based on edge center network feature extraction has achieved good results. Among them， LR has the best classification effect for non-ictal and ictal epilepsy， with an accuracy of 99.30%. The results show that the proposed method can effectively extract feature information and provide new ideas for clinical early warning of epilepsy.

Key words: Key words: epilepsy, classification, complex network, feature extraction, connected edge matrix

CLC Number:

TN305

LIU Lipei, YANG Xiaoli, LI Zhenwei. EEG Classification of Epilepsy Based on Edge-center Network Feature Extraction[J]. Computer and Modernization, 2024, 0(05): 22-26.

References

［1］ MAHMOODIAN N， BOESE A， FRIEBE M， et al. Epileptic seizure detection using cross-bispectrum of electroencephalogram signal［J］. Seizure， 2019，66:4-11.
［2］景鹏，张学军，孙知信. 基于弹性变分模态分解的癫痫脑电信号分类方法［J］ .物理学报， 2021，70（1）:363-370.
［3］张媛，聂莉媛，张青，等. 中医药治疗癫痫的系统评价［J］.中华中医药杂志， 2016，31（12）:5266-5270.
［4］ HUSSEIN R， PALANGI H， WARD R K， et al. Optimized deep neural network architecture for robust detection of epileptic seizures using eeg signals［J］. Clinical Neurophysiology， 2019，130（1）:25-37.
［5］许德刚，王露，李凡. 深度学习的典型目标检测算法研究综述［J］. 计算机工程与应用， 2021，57（8）:10-25.
［6］ PATHAK A， RAMESH A， MITRA A， et al. Automatic seizure detection by modified line length and mahalanobis distance function［J］. Biomedical Signal Processing and Control， 2018，44:279-287.
［7］邓赵红，陈俊勇，刘解放，等. 面向癫痫脑电图信号识别的径向基最小最大概率分类树［J］. 电子与信息学报， 2016， 38（11）:2848-2855.
［8］ ALI KHAN N， ALI S. A new feature for the classification of non-stationary signals based on the direction of signal energy in the time-frequency domain［J］. Computers in Biology & Medicine， 2018，100:10-16.
［9］林栋，黄晓真，庄婉玉，等. 基于近似熵及关联维度对浅针疗法干预失眠患者脑电数据的提取及分析［J］. 针刺研究， 2018（3）:180-184.
［10］李树春，李殿奎，朱晓峰，等. 基于关联维数和lyapunov 指数高频脑电非线性动力学研究［J］. 现代生物医学进展， 2017，17（3）:561-566.
［11］ KORDA A I， ASVESTAS P A， MATSOPOULOS G K， et al. Automatic identification of eye movements using the largest lyapunov exponent［J］. Biomedical Signal Processing & Control， 2018，41:10-20.
［12］崔刚强，夏良斌，梁建峰，等. 基于小波多尺度分析和极限学习机的癫痫脑电分类算法［J］. 生物医学工程学杂志， 2016，33（6）:1025-1030.
［13］ ACHARYA U R， SUDARSHAN V K， KOH J E W， et al. Application of higher-order spectra for the characterization of coronary artery disease using electrocardiogram signals-sciencedirect［J］. Biomedical Signal Processing & Control， 2017， 31:31-43.
［14］ SAMEER M， GUPTA B . Detection of epileptical seizures based on alpha band statistical features［J］.Wireless Personal Communications， 2020，115（2）:927.
［15］ SRIRAAM N， RAGHU S. Classification of focal and non focal epileptic seizures using multi-features and SVM classifier［J］. Journal of Medical Systems， 2017，41（10）:160.
［16］ ULLAH I， HUSSAIN M， ABOALSAMH H. An automated system for epilepsy detection using eeg brain signals based on deep learning approach［J］. Expert Systems with Applications， 2018，107:61-71.
［17］姚蓉，杨雄，杨鹏飞，等. 精分EEG脑网络同步稳定性研究［J］. 计算机工程与应用， 2019， 55（5）:60-64.
［18］ FASKOWITZ J， ESFAHLANI F Z， JO Y， et al. Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture［J］. Nature Neuroscience， 2020，23（12）:1644-1654.
［19］ PALVA JM， MONTO S， KULASHEKHAR S， et al. Neuronal synchrony reveals working memory networks and predicts individual memory capacity［J］. Proceedings of the National Academy of Sciences of the United States of America， 2010，107（16）:7580-7585.
［20］邹凌，吴帆，毕卉，等. 基于皮尔逊最优电极选择的adhd患者脑电特征提取及分类研究［J］. 图学学报， 2020，41（3）:417-423.
［21］ STAM C J， HAAN W D， DAFFERTSHOFER A， et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in alzheimer’s disease［J］. Brain， 2009，132（1）:213-224.
［22］李明爱，南琳，孙炎珺. 基于锁相值和图论的脑功能网络特征提取方法［J］. 北京生物医学工程， 2019，38（1）:15-21.
［23］杜轻，辛守庭，雷新宇，等. 基于脑网络和TSK模糊系统的癫痫脑电识别［J］. 计算机工程与应用， 2020， 56（2）:133-140.
［24］易芳吉，钟丽莎，李章勇. 基于SVM分类器的癫痫脑电时空特征提取方法的研究［J］. 重庆邮电大学学报（自然科学版）， 2022， 34（3）:444-450.
［25］ TAJMIRRIAHI M， AMINI Z. Modeling of seizure and seizure-free eeg signals based on stochastic differential equations［J］. Chaos Solitons & Fractals， 2021，150: 111104. DOI:10.1016/j.chaos.2021.111104.
［26］潘奕竹，沈娜. 离散差分模块在癫痫脑电分类中的应用［J］. 电子测量技术， 2021，44（1）:70-75.
［27］ RICHARIYA B， TANVEER M. EEG sihnal classification using universum support vector machine［J］. Expert System with Applications， 2018，106:169-182.
［28］ ROCCA M L， GARNER R， AMOROSO N， et al. Multiplex networks to characterize seizure development in traumatic brain injury patients［J］. Frontiers in Neuroscience， 2020，14. DOI:10.3389/fnins.2020.591662.

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