A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

Preprocessing and time-frequency analysis of newborn EEG seizures

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.

Fibroblast growth factor receptor 4 (FGFR4) expression in newborn murine calvaria and primary osteoblast cultures

Fibroblast growth factor receptor (FGFR) signalling is important in the initiation and regulation of osteogenesis. Although mutations in FGFR1, 2 and 3 genes are known to cause skeletal deformities, the expression of FGFR4 in bony tissue remains unclear. We have investigated the expression pattern of FGFR4 in the neonatal mouse calvaria and compared it to the expression pattern in cultures of primary osteoblasts. Immunohistochemistry demonstrated that FGFR4 was highly expressed in rudimentary membranous bone and strictly localised to the cellular components (osteoblasts) between the periosteal and endosteal layers. Cells in close proximity to the newly formed osteoid (preosteoblasts) also expressed FGFR4 on both the endosteal and periosteal surfaces. Immunocytochemical analysis of primary osteoblast cultures taken from the same cranial region also revealed high levels of FGFR4 expression, suggesting a similar pattern of cellular expression in vivo and in vitro. RT-PCR and Western blotting for FGFR4 confirmed its presence in primary osteoblast cultures. These results suggest that FGFR4 may be an important regulator of osteogenesis with involvement in preosteoblast proliferation and differentiation as well as osteoblast functioning during intramembranous ossification. The consistent expression of FGFR4 in vivo and in vitro supports the use of primary osteoblast cultures for elucidating the role of FGFR4 during osteogenesis.

Systematic review of the role of pre-oxygenation for tracheal suctioning in ventilated newborn infants

Pre-oxygenation for endotracheal suctioning for mechanically ventilated infants is routine practice in many neonatal intensive care units. In the present systematic review the evidence to support its use is discussed and the authors conclude that no confident recommendations can be made from the results of this review.

Adult-population incidence of severe sepsis in Australian and New Zealand intensive care units

Objective. To determine the population incidence and outcome of severe sepsis occurring in adult patients treated in Australian and New Zealand intensive care units (ICUs), and compare with recent retrospective estimates from the USA and UK. Design. Inception cohort study. Setting. Twenty-three closed multi-disciplinary ICUs of 21 hospitals (16 tertiary and 5 university affiliated) in Australia and New Zealand. Patients. A total of 5878 consecutive ICU admission episodes. Measurements and results. Main outcome measures were population-based incidence of severe sepsis, mortality at ICU discharge, mortality at 28 days after onset of severe sepsis, and mortality at hospital discharge. A total of 691 patients, 11.8 (95% confidence intervals 10.9-12.6) per 100 ICU admissions, were diagnosed with 752 episodes of severe sepsis. Site of infection was pulmonary in 50.3% of episodes and abdominal in 19.3% of episodes. The calculated incidence of severe sepsis in adults treated in Australian and New Zealand ICUs is 0.77 (0.76-0.79) per 1000 of population. 26.5% of patients with severe sepsis died in ICU, 32.4% died within 28 days of the diagnosis of severe sepsis and 37.5% died in hospital. Conclusion. In this prospective study, 11.8 patients per 100 ICU admissions were diagnosed with severe sepsis and the calculated annual incidence of severe sepsis in adult patients treated in Australian and New Zealand ICUs is 0.77 per 1000 of population. This figure for the population incidence falls in the lower range of recent estimates from retrospective studies in the U.S. and the U.K.

Serum procalcitonin and C-reactive protein as markers of sepsis and outcome in patients with neurotrauma and subarachnoid haemorrhage

This prospective study evaluated serum procalcitonin (PCT) and C-reactive protein (CRP) as markers for systemic inflammatory response syndrome (SIRS)/sepsis and mortality in patients with traumatic brain injury and subarachnoid haemorrhage. Sixty-two patients were followed for 7 days. Serum PCT and CRP were measured on days 0, 1, 4, 5, 6 and 7. Seventy-seven per cent of patients with traumatic brain injury and 83% with subarachnoid haemorrhage developed SIRS or sepsis (P= 0.75). Baseline PCT and CRP were elevated in 35% and 55% ofpatients respectively (P=0.03). There was a statistically non-significant step-wise increase in serum PCT levels from no SIRS (0.4 +/- 0.6 ng/ml) to SIRS (3.05 +/- 9.3 ng/ml) to sepsis (5.5 +/- 12.5 ng/ml). A similar trend was noted in baseline PCT in patients with mild (0.06 +/- 0.9 ng/ml), moderate (0.8 +/- 0.7 ng/ml) and severe head injury (1.2 +/- 1.9 ng/ml). Such a gradation was not observed with serum CRP There was a non-significant trend towards baseline PCT being a better marker of hospital mortality compared with baseline CRP (ROC-AUC 0.56 vs 0.31 respectively). This is the first prospective study to document the high incidence of SIRS in neurosurgical patients. In our study, serum PCT appeared to correlate with severity of traumatic brain injury and mortality. However, it could not reliably distinguish between SIRS and sepsis in this cohort. This is in pan because baseline PCT elevation seemed to correlate with severity of injury. Only a small proportion ofpatients developed sepsis, thus necessitating a larger sample size to demonstrate the diagnostic usefulness of serum PCT as a marker of sepsis. Further clinical trials with larger sample sizes are required to confirm any potential role of PCT as a sepsis and outcome indicator in patients with head injuries or subarachnoid haemorrhage.