Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine.

IF 1.7 Q4 NEUROSCIENCES
Neurology Research International Pub Date : 2018-07-02 eCollection Date: 2018-01-01 DOI:10.1155/2018/1613456
Jisu Elsa Jacob, Gopakumar Kuttappan Nair, Thomas Iype, Ajith Cherian
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引用次数: 0

Abstract

EEG analysis in the field of neurology is customarily done using frequency domain methods like fast Fourier transform. A complex biomedical signal such as EEG is best analysed using a time-frequency algorithm. Wavelet decomposition based analysis is a relatively novel area in EEG analysis and for extracting its subbands. This work aims at exploring the use of discrete wavelet transform for extracting EEG subbands in encephalopathy. The subband energies were then calculated and given as feature sets to SVM classifier for identifying cases of encephalopathy from normal healthy subjects. Out of various combinations of subband energies, energy of delta subband yielded highest performance parameters for SVM classifier with an accuracy of 90.4% in identifying encephalopathy cases.

Abstract Image

Abstract Image

Abstract Image

利用离散小波变换和支持向量机基于脑电图子带能量诊断脑病
神经学领域的脑电图分析通常采用快速傅立叶变换等频域方法。像脑电图这样复杂的生物医学信号最好使用时频算法进行分析。基于小波分解的分析是脑电图分析和提取其子带的一个相对新颖的领域。这项研究旨在探索使用离散小波变换提取脑病的脑电图子带。然后计算子带能量,并将其作为 SVM 分类器的特征集,用于从正常健康人中识别脑病病例。在各种子带能量组合中,δ子带能量为 SVM 分类器提供了最高的性能参数,识别脑病病例的准确率高达 90.4%。
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来源期刊
CiteScore
3.50
自引率
0.00%
发文量
10
审稿时长
17 weeks
期刊介绍: Neurology Research International is a peer-reviewed, Open Access journal that publishes original research articles, review articles, and clinical studies focusing on diseases of the nervous system, as well as normal neurological functioning. The journal will consider basic, translational, and clinical research, including animal models and clinical trials.
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