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Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine
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 ex...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051006/ https://www.ncbi.nlm.nih.gov/pubmed/30057813 http://dx.doi.org/10.1155/2018/1613456 |
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author | Jacob, Jisu Elsa Nair, Gopakumar Kuttappan Iype, Thomas Cherian, Ajith |
author_facet | Jacob, Jisu Elsa Nair, Gopakumar Kuttappan Iype, Thomas Cherian, Ajith |
author_sort | Jacob, Jisu Elsa |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6051006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-60510062018-07-29 Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine Jacob, Jisu Elsa Nair, Gopakumar Kuttappan Iype, Thomas Cherian, Ajith Neurol Res Int Research Article 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. Hindawi 2018-07-02 /pmc/articles/PMC6051006/ /pubmed/30057813 http://dx.doi.org/10.1155/2018/1613456 Text en Copyright © 2018 Jisu Elsa Jacob et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Jacob, Jisu Elsa Nair, Gopakumar Kuttappan Iype, Thomas Cherian, Ajith Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine |
title | Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine |
title_full | Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine |
title_fullStr | Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine |
title_full_unstemmed | Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine |
title_short | Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine |
title_sort | diagnosis of encephalopathy based on energies of eeg subbands using discrete wavelet transform and support vector machine |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051006/ https://www.ncbi.nlm.nih.gov/pubmed/30057813 http://dx.doi.org/10.1155/2018/1613456 |
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