| 基于希尔伯特边际谱和极限学习机的癫痫脑电信号分类 |
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| 引用本文： | 火元莲,陈萌萌,郑海亮,连培君,张健. 基于希尔伯特边际谱和极限学习机的癫痫脑电信号分类[J]. 光电子．激光, 2021, 32(10): 1083-1091. DOI: 10.16136/j.joel.2021.10.0123 |
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| 作者姓名： | 火元莲 陈萌萌 郑海亮 连培君 张健 |
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| 作者单位： | 西北师范大学物理与电子工程学院,甘肃兰州730070 |
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| 基金项目： | 国家自然科学基金(61561044)资助项目 (西北师范大学 物理与电子工程学院,甘肃 兰州 730070) |
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| 摘 要： | 提出了一种基于希尔伯特边际谱和极限学习机相结合的癫痫脑电信号分类方法.首先将脑电信号进行经验模态分解,对前5个本征模态函数进行希尔伯特变换,得到其希尔伯特边际谱;然后将希尔伯特边际谱的Shannon熵、Renyi熵和Tsallis熵,以及5个不同频段节律信号的能量作为有效特征输入极限学习机进行分类.实验结果表明,本文方...
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| 关 键 词： | 癫痫脑电(electroencephalogram,EEG)信号 希尔伯特边际谱 极限学习机 谱熵 子带能量 |
| 收稿时间： | 2021-01-06 |
Epileptic EEG signal classification based on Hilbert marginal spectrum and extre me learning machine |
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| HUO Yuanlian,CHEN Mengmeng,ZHENG Hailiang,LIAN Peijun and ZHANG Jian. Epileptic EEG signal classification based on Hilbert marginal spectrum and extre me learning machine[J]. Journal of Optoelectronics·laser, 2021, 32(10): 1083-1091. DOI: 10.16136/j.joel.2021.10.0123 |
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| Authors: | HUO Yuanlian CHEN Mengmeng ZHENG Hailiang LIAN Peijun ZHANG Jian |
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| Affiliation: | College of Physics and Electronic Engineering,Northwest Normal University,Lanz hou,Gansu 730070,China,College of Physics and Electronic Engineering,Northwest Normal University,Lanz hou,Gansu 730070,China,College of Physics and Electronic Engineering,Northwest Normal University,Lanz hou,Gansu 730070,China,College of Physics and Electronic Engineering,Northwest Normal University,Lanz hou,Gansu 730070,China and College of Physics and Electronic Engineering,Northwest Normal University,Lanz hou,Gansu 730070,China |
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| Abstract: | This paper presents a classification method of epilepsy electroencephalogram (EEG) signals ba sed on the combination of Hilbert marginal spectrum and extreme learning machine .Firstly,the empirical mode decomposition of EEG signals is carried out,and t he Hilbert marginal spectrum is obtained by applying the Hilbert transform to th e first 5intrinsic mode function; Then Shannon entropy,Renyi entropy and Tsall is entropy of Hilbert marginal spectrum as well as the energy of 5different fre quency sub-band rhythm signals were input into the extreme learning machine as e ffective characteristics for classification.The experimental results show that the classification accuracy of the epileptic signal in this paper reaches 99.8%, which is higher than other classification methods in detection accuracy and com puting speed,and has potential application value in real-time detection of epi leptic seizures. |
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| Keywords: | epileptic electroencephalogram (EEG) signal Hilbert marginal spectrum extreme learning machine spectral ent ropies sub-band energies |
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