Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine. (2nd July 2018)

• Record Type: Journal Article
• Authors: Jacob, Jisu Elsa; Nair, Gopakumar Kuttappan; Iype, Thomas; Cherian, Ajith
• Other Names: Brok Herbert Academic Editor.
• Is Part Of: Neurology research international. Volume 2018(2018)
• Publication Date: 2018-07-02
• Subjects: Neurology -- Periodicals; Nervous system -- Diseases -- Periodicals; 616.8005
• DOI: 10.1155/2018/1613456
• Languages: English
• ISSNs: 2090-1852

Abstract

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.