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.
Highlights
Electroencephalogram (EEG) is a signal which represents the electrical activity of millions of neurons in the brain
We have explored the application of discrete wavelet transform in EEG analysis in cases of encephalopathies
Our study concludes the relevance of wavelet decomposition in EEG analysis where time localisation of frequency components of the signal is possible
Summary
Electroencephalogram (EEG) is a signal which represents the electrical activity of millions of neurons in the brain. The signal is acquired from the surface of the scalp. Since it reflects the neuronal activity of the cerebral cortex, it is used in the diagnosis of diseases which involves the function of the cortical neurons. Time domain and frequency domain analysis are not sufficient to give information of such signals. First difference and second difference are computed in time series analysis to get the signal variation over time [2]. Another time domain feature, namely, Normalized Length Density, was proposed by Jenke et al, which quantifies self-similarities within the EEG signal [3]
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