Abstract
Application of methods of signal processing used in radioengineering for electroencephalograms (EEG) provides to increase the information content of the analysis and medical diagnostic quality. In this paper the method of EEG analysis based on the Hilbert transform are considered. Comparing results analysis with the Fourier transform (FT) and wavelet transform (WT) is carried out. Using the Hilbert transform for calculations, the analysis data on the informative parameters of EEG of healthy persons and those of epileptic patients were obtained. The calculated time dependences of the total phase and instantaneous frequency are presented on the diagrams. It is shown that application of the Hilbert transform provides an evident and simple interpretation of EEG diagnostics results. The phase-frequency method of EEG analysis gives an opportunity to track the dynamics of EEG change, to numerically characterize the duration and variation of the basic physiological rhythms, and, also, to observe the frequency change in time within the limits of each rhythm.
Highlights
Since its discovery, EEG has been useful tool in understanding of brain functioning at all and diagnosing neurophysiological disorders
The main difference is this: Fourier transform decomposes the signal into sines and cosines, i.e. the functions localized in Fourier space; in contrary the wavelet transform uses functions that are localized in both the real and Fourier space
The phase-frequency method of EEG analysis gives an opportunity to track the dynamics of EEG change, to haracterize numerically the duration and variation of the basic physiological rhythms, and, to observe the frequency change in time within the limits of each rhythm
Summary
EEG has been useful tool in understanding of brain functioning at all and diagnosing neurophysiological disorders. Signal processing based on radioengineering methods resulted in an incentive to use computer technology, automate detection and analysis, and use more objective quantitative approaches. The use of computers in EEG enables real-time denoising, automatic rhythmic analysis, and more complicated quantifications. Nowadays the digital methods of EEG analysis (in main, spectral and correlation analysis) are finding ever increasing application. An advantage of these methods consists in the elimination of subjectivity, being inevitable for a visual analysis. The wavelet transform is used to analyze nonstationary processes [3]. This method is not yet used extensively in the diagnostics by specialists because of complexity in interpretations of results
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