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Objective: Skin-to-skin contact (SSC) promotes physiological stability and interaction between parents and infants. Analyses of EEG-sleep studies can compare functional brain maturation between SSC and non-SSC cohorts.Methods: Sixteen EEG-sleep studies were performed on eight preterm infants who received eight weeks of SSC, and compared with two non-SSC cohorts at term (N=126), a preterm group corrected to term age and a full term group. Seven linear and two complexity measures were compared (Mann-Whitney U test comparisons p<.05).Results: Fewer REMs, more quiet sleep, increased respiratory regularity, longer cycles, and less spectral beta were noted for SSC preterm infants compared with both control cohorts. Fewer REMs, greater arousals and more quiet sleep were noted for SSC infants compared with the non-SSC preterms at term. Three right hemispheric regions had greater complexity in the SSC group. Discriminant analysis showed that the SSC cohort was closer to the non-SSC full-term cohort. Conclusion:Skin to skin contact accelerates brain maturation in healthy preterm infants compared with two groups without SSC.Significance: Combined use of linear and complexity analysis strategies offer complementary information regarding altered neuronal functions after developmental care interventions. Such analyses may be helpful to assess other neuroprotection strategies.

Background:We have previously shown an increased incidence of intermittent hypoxemia (Ih) events in preterm infants with severe retinopathy of prematurity (ROP). animal models suggest that patterns of Ih events may play a role in ROP severity as well. We hypothesize that specific Ih event patterns are associated with ROP in preterm infants. Methods: Variability in Ih event duration, severity, and the time interval between Ih events (≤80%, ≥10 s, and ≤3 min) along with the frequency spectrum of the oxygen saturation (spO 2 ) waveform were assessed. results: severe ROP was associated with (i) an increased mean and sD of the duration of Ih event (P < 0.005), (ii) more variability (histogram entropy) of the time interval between Ih events (P < 0.005), (iii) a higher Ih nadir (P < 0.05), (iv) a time interval between Ih events of 1-20 min (P < 0.05), and (v) increased spectral power in the range of 0.002-0.008 hz (P < 0.05), corresponding to spO 2 waveform oscillations of 2-8 min in duration. spectral differences were detected as early as 14 d of life. conclusion: severe ROP was associated with more variable, longer, and less severe Ih events. Identification of specific spectral components in the spO 2 waveform may assist in early identification of infants at risk for severe ROP.

Summary Peri-ictal autonomic dysregulation is best studied using a “polygraphic” approach (EEG, 3-channel EKG, pulse Oximetry, respiration and continuous non-invasive blood pressure [BP]) and may help elucidate agonal pathophysiological mechanisms leading to Sudden Unexpected Death in Epilepsy (SUDEP). A number of autonomic phenomena have been described in generalized tonic-clonic seizures (GTCS), the commonest seizure type associated with SUDEP, including decreased heart rate variability, cardiac arrhythmias and changes in skin conductance. Post-ictal generalized EEG suppression (PGES) has been identified as a potential risk marker of SUDEP and PGES has been found to correlate with post GTCS autonomic dysregulation in some patients. Here, we describe a patient with a GTCS in whom polygraphic measurements, including continuous non-invasive blood pressure recordings, were obtained. Significant post-ictal hypotension lasting >60 seconds was found which closely correlated with PGES duration. Similar EEG changes are well described in hypotensive patients with vasovagal syncope and a similar vasodepressor phenomenon and consequent cerebral hypo-perfusion may account for the PGES observed in some patients after a GTCS. This further raises the possibility that profound, prolonged and irrecoverable hypotension may comprise one potential SUDEP mechanism.

There is a broad consensus that 21st century health care will require intensive use of information technology to acquire and analyze data and then manage and disseminate information extracted from the data. No area is more data intensive than the intensive care unit. While there have been major improvements in intensive care monitoring, the medical industry, for the most part, has not incorporated many of the advances in computer science, biomedical engineering, signal processing, and mathematics that many other industries have embraced. Acquiring, synchronizing, integrating, and analyzing patient data remain frustratingly difficult because of incompatibilities among monitoring equipment, proprietary limitations from industry, and the absence of standard data formatting. In this paper, we will review the history of computers in the intensive care unit along with commonly used monitoring and data acquisition systems, both those commercially available and those being developed for research purposes.

Generalized tonic-clonic seizures (GTCS) are the commonest seizure type associated with Sudden Unexplained Death in Epilepsy (SUDEP). This study examines semiological and electroencephalographic differences (EEG) in the GTCS of adults as compared to children. The rationale lies in epidemiological observations that have noted a ten-fold higher incidence of SUDEP in adults. We analyzed video-EEG data of 105 GTCS in 61 consecutive patients (12 children, 23 seizures and 49 adults, 82 seizures) recruited from the Epilepsy Monitoring Unit. Semiological, EEG and 3-channel EKG features were studied. Peri-ictal seizure phase durations were analyzed including tonic, clonic, total seizure, post-ictal EEG suppression (PGES) and recovery phases. Heart rate variability (HRV) measures including RMSSD (root mean square successive difference of R-R intervals), SDNN (standard deviation of NN intervals) and SDSD (standard deviation of differences) were analyzed (including low frequency/high frequency power ratios) during pre-ictal baseline, ictal and post-ictal phases. Generalized estimating equations (GEE) were used to find associations between electro-clinical features. Separate subgroup analyses were carried out on adult and pediatric age groups as well as medication groups (no anti-epileptic medication cessation versus unchanged or reduced medication) during admission. Major differences were seen in adult and pediatric seizures with total seizure duration, tonic phase, PGES and recovery phases being significantly shorter in children (p<0.01). GEE analysis using tonic phase duration as the dependent variable, found age to correlate significantly (p<0.001) and this remained significant during subgroup analysis (adults and children) such that each 0.12 second increase in tonic phase duration correlated with a 1 second increase in PGES duration. PGES durations were on average 28 seconds shorter in children. With cessation of medication, total seizure duration was significantly increased by a mean value of 8 seconds in children and 11 seconds in adults (p<0.05). Tonic phase duration also significantly increased with medication cessation and although PGES durations increased, this was not significant. RMSSD was negatively correlated with PGES duration (longer PGES durations were associated with decreased vagally mediated heart rate variability; p<0.05) but not with tonic phase duration. This study clearly points out identifiable electro-clinical differences between adult and pediatric GTCS that may be relevant in explaining lower SUDEP risk in children. The findings suggest that some prolonged seizure phases and prolonged PGES duration may be electro-clinical markers of SUDEP risk and merit further study.

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Although there is no strict consensus, some studies have reported that Postictal generalized EEG suppression (PGES) is a potential electroencephalographic (EEG) biomarker for risk of sudden unexpected death in epilepsy (SUDEP). PGES is an epoch of EEG inactivity after a seizure, and the detection of PGES in clinical data is extremely difficult due to artifacts from breathing, movement and muscle activity that can adversely affect the quality of the recorded EEG data. Even clinical experts visually interpreting the EEG will have diverse opinions on the start and end of PGES for a given patient. The development of an automated EEG suppression detection tool can assist clinical personnel in the review and annotation of seizure files, and can also provide a standard for quantifying PGES in large patient cohorts, possibly leading to further clarification of the role of PGES as a biomarker of SUDEP risk. In this paper, we develop an automated system that can detect the start and end of PGES using frequency domain features in combination with boosting classification algorithms. The average power for different frequency ranges of EEG signals are extracted from the prefiltered recorded signal using the fast fourier transform and are used as the feature set for the classification algorithm. The underlying classifiers for the boosting algorithm are linear classifiers using a logistic regression model. The tool is developed using 12 seizures annotated by an expert then tested and evaluated on another 20 seizures that were annotated by 11 experts.