Intracranial hemorrhage (IH) is a major problem of neonatal intensive care. The incidence of IH is typically asymptomatic and cannot be e®ectively detected by standard diagnostic methods. The mechanisms underlying IH are unknown but there is evidence that stress-induced disorders in adrenergic regulation of cerebral venous blood°ow (CVBF) are among the main reasons. Quantitative and qualitative assessment of CVBF could signi¯cantly advance understanding of the nature of IH in newborns. In this work, we analyze variations of CVBF in newborn rats with an experimental model of stress-induced IH and adrenaline injection. Our analysis is based on the Doppler optical coherence tomography (DOCT) and a proposed adaptive wavelet-based approach that provides sensitive markers of abnormal reactions of the sagittal vein to external factors. The obtained results demonstrate that the incidence of IH in newborn rats is accompanied by a suppression of CVBF with the development of venous insu±ciency and areactivity to adrenaline. We introduce a numerical measure , quantifying reactions of CVBF and show that the values < 1:23 estimated in the low-frequency (LF) spectral range corresponding to the sympathicus indicate abnormal reactions associated with the development of IH. We conclude that the revealed
The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.
The problem concerning recognition of single pulses under the action of interferences is discussed by the example of classification of neuron action potentials. Joint applications of wavelets and artificial neural networks in solving the the given problem are analyzed. The recognition method, which is based on wavelet neural networks and ensures adjustment of the synapses of a supplementary (''wavelet'') layer, has been pro posed. It is demonstrated that experimental data can efficiently be analyzed via the proposed method.
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