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

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...

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Main Authors: Jisu Elsa Jacob, Gopakumar Kuttappan Nair, Thomas Iype, Ajith Cherian
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Neurology Research International
Online Access:http://dx.doi.org/10.1155/2018/1613456
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spelling doaj-c8f7a63e04fa4774be9b461949d040e82020-11-24T20:56:03ZengHindawi LimitedNeurology Research International2090-18522090-18602018-01-01201810.1155/2018/16134561613456Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector MachineJisu Elsa Jacob0Gopakumar Kuttappan Nair1Thomas Iype2Ajith Cherian3Department of Electronics and Communication Engineering, SCT College of Engineering, Thiruvananthapuram, Kerala, IndiaDepartment of Electronics and Communication Engineering, TKM College of Engineering, Kollam, Kerala, IndiaDepartment of Neurology, Government Medical College, Thiruvananthapuram, Kerala, IndiaDepartment of Neurology, SCTIMST, Thiruvananthapuram, Kerala, IndiaEEG 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.http://dx.doi.org/10.1155/2018/1613456
collection DOAJ
language English
format Article
sources DOAJ
author Jisu Elsa Jacob
Gopakumar Kuttappan Nair
Thomas Iype
Ajith Cherian
spellingShingle Jisu Elsa Jacob
Gopakumar Kuttappan Nair
Thomas Iype
Ajith Cherian
Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine
Neurology Research International
author_facet Jisu Elsa Jacob
Gopakumar Kuttappan Nair
Thomas Iype
Ajith Cherian
author_sort Jisu Elsa Jacob
title 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_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_sort diagnosis of encephalopathy based on energies of eeg subbands using discrete wavelet transform and support vector machine
publisher Hindawi Limited
series Neurology Research International
issn 2090-1852
2090-1860
publishDate 2018-01-01
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.
url http://dx.doi.org/10.1155/2018/1613456
work_keys_str_mv AT jisuelsajacob diagnosisofencephalopathybasedonenergiesofeegsubbandsusingdiscretewavelettransformandsupportvectormachine
AT gopakumarkuttappannair diagnosisofencephalopathybasedonenergiesofeegsubbandsusingdiscretewavelettransformandsupportvectormachine
AT thomasiype diagnosisofencephalopathybasedonenergiesofeegsubbandsusingdiscretewavelettransformandsupportvectormachine
AT ajithcherian diagnosisofencephalopathybasedonenergiesofeegsubbandsusingdiscretewavelettransformandsupportvectormachine
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