Signal processing

MODELING FROM TIME SERIES AND APPLICATIONS TO PROCESSING OF COMPLEX SIGNALS

Signals obtained from most of real­world systems, especially from living organisms, are irregular, often chaotic, non­stationary, and noise­corrupted. Since modern measuring devices usually realize digital processing of information, recordings of the signals take the form of a discrete sequence of samples (a time series). The present paper gives a brief overview of the possibilities of such experimental data processing based on reconstruction and usage of a predictive empirical model of a time realization under study.

DIAGNOSTICS AND ANALYSIS OF OSCILLATORY NEURONAL NETWORK ACTIVITY OF BRAIN WITH CONTINUOUS WAVELET ANALYSIS

In the article we present an overview of a number of continuous wavelet transform­based techniques for analysis and diagnostic of oscillatory neuronal network activity of brain in experimentally obtained  electroencephalographic data. We describe a technique for automatic detection of characteristic patterns for paroxysmal activity (spike­wave discharges) in epileptic electroencephalogram (EEG) based on wavelet spectrum power analysis, obtained with continuous wavelet transform with complex mother wavelet (Morlet) in specific frequency ranges.