Digital signal processing techniques are often used for measurement of small time shifts between EEG signals. In our work we tested properties of linear cross-correlation and phase/coherence method. The last mentioned method was used in two versions. The first version used fast Fourier transform (FFT) algorithm and the second was based on autoregressive modeling with fixed or adaptive model order. Methods were compared on several testing signals mimicking real EEG signals. The accuracy index for each method was computed. Results showed that for long signal segments all methods bring comparably good results. Accuracy of FFT phase/coherence method significantly decreased when very short segments were used and also decreased with an increasing level of the additive noise. The best results were obtained with autoregressive version of phase/coherence. This method is more reliable and may be used with high accuracy even in very short signals segments and it is also resistant to additive noise.
Hypoxic-ischemic encephalopathy (HIE) is one of the leading pediatric neurological conditions causing long-term disabilities and socio-economical burdens. Nearly 20-50 % of asphyxiated newborns with HIE die within the newborn period and another third will develop severe health consequences and permanent handicaps. HIE is the result of severe systemic oxygen deprivation and reduced cerebral blood flow, commonly occurring in full-term infants. Hypoxic-ischemic changes trigger several molecular and cellular processes leading to cell death and
inflammation. Generated reactive oxygen species attack surrounding cellular components resulting in functional deficits and mitochondrial dysfunction. The aim of the present paper is to review present knowledge about the pathophysiology of perinatal hypoxic-ischemic encephalopathy, especially with respect to novel treatment strategies and biomarkers that might enhance early detection of this disorder and thus improve the general outcome of patients.