基于局部模式的癫痫脑电信号自动分类方法

被引：4

作者：

齐永锋

李陇强

机构：

[1] 西北师范大学计算机科学与工程学院

来源：

计算机工程 | 2020年 / 46卷 / 02期

关键词：

脑电图; 局部三值模式算子; 特征提取; 分类; 癫痫;

D O I：

10.19678/j.issn.1000-3428.0053501

中图分类号：

R742.1 [癫痫]; TP181 [自动推理、机器学习]; TN911.7 [信号处理];

学科分类号：

081002 [信号与信息处理]; 100204 [神经病学]; 140502 [人工智能];

摘要：

为有效地检测脑电图(EEG)中的癫痫信号,设计一维局部三值模式(1D-LTP)算子提取信号特征,并结合主成分分析(PCA)和极限学习机(ELM)对特征进行分类。通过1D-LTP算子计算信号点的顶层模式和底层模式下的特征变换码以准确滤除干扰信号,并对变换码直方图PCA降维后采用ELM进行分类,以10折交叉验证评估分类性能。实验结果表明,该方法能有效识别在癫痫发作期的EEG信号,其准确率可达99.79%。

引用

页码：298 / 303

页数：6

共 19 条

[1]

Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals.[J].Abeg Kumar Jaiswal;Haider Banka.Biomedical Signal Processing and Control.2017,

[2]

A hybrid automated detection of epileptic seizures in EEG records.[J].Noha S. Tawfik;Sherin M. Youssef;Mohamed Kholief.Computers and Electrical Engineering.2016,

[3]

Classification of seizure and seizure-free EEG signals using local binary patterns.[J].T. Sunil Kumar;Vivek Kanhangad;Ram Bilas Pachori.Biomedical Signal Processing and Control.2015,

[4]

Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine.[J].Yatindra Kumar;M.L. Dewal;R.S. Anand.Neurocomputing.2014,

[5]

1D-local binary pattern based feature extraction for classification of epileptic EEG signals.[J].Yılmaz Kaya;Murat Uyar;Ramazan Tekin;Selçuk Yıldırım.Applied Mathematics and Computation.2014,

[6]

A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection.[J].Lan-Lan Chen;Jian Zhang;Jun-Zhong Zou;Chen-Jie Zhao;Gui-Song Wang.Biomedical Signal Processing and Control.2014,

[7]

Classification of ictal and seizure-free EEG signals using fractional linear prediction.[J].Varun Joshi;Ram Bilas Pachori;Antony Vijesh.Biomedical Signal Processing and Control.2014,

[8]

Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines.[J].Nicoletta Nicolaou;Julius Georgiou.Expert Systems With Applications.2011, 1

[9]

Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition [J].

Pachori, Ram Bilas ;

Bajaj, Varun .

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2011, 104 (03) :373-381

[10]

Epileptic EEG detection using the linear prediction error energy [J].

Altunay, Semih ;

Telatar, Ziya ;

Erogul, Osman .

EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (08) :5661-5665

← 1 2 →