Vladimir Matic

Learn More
OBJECTIVE EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to(More)
We propose a novel approach for compressive sampling of the neonatal electro-encefalogram (EEG) data. The method assumes that the set of EEG data is generated by linearly mixing a fewer number of source signals. Another assumption is that the sources are nearly-sparse in Gabor dictionary. The presented method, instead of compressing original EEG channels,(More)
A key feature of normal neonatal EEG at term age is interhemispheric synchrony (IHS), which refers to the temporal co-incidence of bursting across hemispheres during trace alternant EEG activity. The assessment of IHS in both clinical and scientific work relies on visual, qualitative EEG assessment without clearly quantifiable definitions. A quantitative(More)
EEG inter-burst interval (IBI) and its evolution is a robust parameter for grading hypoxic encephalopathy and prognostication in newborns with perinatal asphyxia. We present a reliable algorithm for the automatic detection of IBIs. This automated approach is based on adaptive segmentation of EEG, classification of segments and use of temporal profiles to(More)
Signal recovery is one of the key techniques of compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains. To further improve the signal(More)
Visual recognition of neonatal seizures during continuous EEG monitoring in neonatal intensive care units (NICUs) is labor-intensive, has low inter-rater agreement and requires special expertise that is not available around the clock. Development of an accurate automated seizure detection system with a low false alarm rate will support clinical decision(More)
In neonatal intensive care units, there is a need for continuous monitoring of sick newborns with perinatal hypoxic ischemic brain injury (HIE). We assessed the utility of heart rate variability (HRV) in newborns with acute HIE undergoing simultaneous continuous EEG (cEEG) and ECG monitoring. HIE was classified using clinical criteria as well as visual(More)
OBJECTIVE The description and evaluation of algorithms using Independent Component Analysis (ICA) for automatic removal of ECG, pulsation and respiration artifacts in neonatal EEG before automated seizure detection. METHODS The developed algorithms decompose the EEG using ICA into its underlying sources. The artifact source was identified using the(More)
Early intermittent cortical activity is thought to play a crucial role in the growth of neuronal network development, and large scale brain networks are known to provide the basis for higher brain functions. Yet, the early development of the large scale synchrony in cortical activations is unknown. Here, we tested the hypothesis that the early intermittent(More)
Compressive sensing has shown significant promise in biomedical fields. It reconstructs a signal from sub-Nyquist random linear measurements. Classical methods only exploit the sparsity in one domain. A lot of biomedical signals have additional structures, such as multi-sparsity in different domains, piecewise smoothness, low rank, etc. We propose a(More)