What does this research mean for the field?

The energy of the delta EEG subband, extracted via discrete wavelet transform and classified using a Support Vector Machine, can identify cases of encephalopathy with 90.4% accuracy. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

July 2, 2018Neurology Research InternationalOpen Access

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

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Why the study?

Does discrete wavelet transform combined with an SVM classifier accurately identify encephalopathy cases compared to normal healthy subjects?

Structured PICO

Does discrete wavelet transform combined with an SVM classifier accurately identify encephalopathy cases compared to normal healthy subjects?

Population

Cases of encephalopathy and normal healthy subjects

Intervention

Discrete wavelet transform for extracting EEG subbands and Support Vector Machine (SVM) classifier

Comparator

Normal healthy subjects

Outcome

Accuracy of identifying encephalopathy cases

Discrete wavelet transform combined with an SVM classifier using delta subband energy can identify encephalopathy cases with 90.4% accuracy.

Implication

Hypothesis-generating for delta subband SVM in encephalopathy detection; requires prospective validation before clinical adoption.

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.