Jisu Elsa Jacob, Gopakumar Kuttappan Nair, Thomas Iype, Ajith Cherian
{"title":"Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine.","authors":"Jisu Elsa Jacob, Gopakumar Kuttappan Nair, Thomas Iype, Ajith Cherian","doi":"10.1155/2018/1613456","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":19124,"journal":{"name":"Neurology Research International","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051006/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurology Research International","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2018/1613456","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/1/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
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.
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.