C. García Vicent’s research while affiliated with Polytechnic University of Valencia and other places

Sepsis, a critical bacterial infection of the bloodstream, is a serious cause of illness in the neonatal period in both premature and at term newborns. It is important to look for parameters that can help earlier detection of sepsis in the newborn. Previous studies have shown that Heart Rate Variability is reduced when associated with sepsis and diminish the adaptive capacity of the individual, degrading the information transported by their signals. To test for the statistical significance in discriminating between healthy and sepsis diagnosed neonates we analyzed the Inter-Beat-Interval derived from 90 minutes electrocardiographic recordings obtained from 45 newborns, 17 with the clinical diagnosis of sepsis and 28 healthy newborns as a control. Statistically significant time-domain measures (p<0.05) of the time series produced paradoxical results comparing sepsis with healthy subjects. Frequency-domain and Time- Frequency analysis showed reduced low-frequency power and a low/high-frequency ratio (p<0.05) in subjects with sepsis; conversely, high-frequency power was significantly higher (p<0.05) in the sepsis group. Nonlinear Sample-Entropy measure showed a significant difference between groups (p<0.01) and lower values in subjects clinically diagnosed with sepsis suggesting lower Inter-Beat- Interval signal complexity.

Sepsis, a critical bacterial infection of the bloodstream, is a frequent cause of illness and death in premature infants in the neonatal intensive care units. A prospective analysis was conducted of the inter–beat-intervals time series (IBI) derived from 1 hour 30 minutes electrocardiogram (ECG) recordings for 14 episodes of clinical sepsis in 90 infants 12-18 hour old in the maternity ward of the Consorcio-Hospital General Universitario de Valencia. The aim was to test the hypothesis that normal beat-to-beat fluctuations in heart rate show fractal long-range correlations and that pathological condition reduce the adaptive capacity of the individual degrading the information carried by their signals and modifying the fractal scaling characteristics. Fractal properties of these series were evaluated by applying the method of Detrended Fluctuation Analysis (DFA) for the quantification of the correlation property in the highly non-stationary IBI time series. The main finding is that heart rate time series from infants with sepsis show a breakdown of the long-range correlation behaviour and consequently complexity-loss. The long-range scaling exponents for the sepsis cases showed a statistically significant deviation (p<0.001) from the long-range scaling exponents of the healthy cases. This method may be of use in distinguishing healthy from pathologic time series of infants with sepsis based on differences in the fractal scaling property, and could be introduced into the clinical practice for the assessment of heart rate patterns of new born infants.

Sepsis, a critical bacterial infection of the bloodstream, is a frequent cause of illness and death in premature infants in the neonatal intensive care units. A prospective analysis was conducted of the inter–beat-intervals time series (IBI) derived from 1 hour 30 minutes electrocardiogram (ECG) recordings for 14 episodes of clinical sepsis in 90 infants 12-18 hour old in the maternity ward of the Consorcio-Hospital General Universitario de Valencia. The aim was to test the hypothesis that normal beat-to-beat fluctuations in heart rate show fractal long-range correlations and that pathological condition reduce the adaptive capacity of the individual degrading the information carried by their signals and modifying the fractal scaling characteristics. Fractal properties of these series were evaluated by applying the method of Detrended Fluctuation Analysis (DFA) for the quantification of the correlation property in the highly non-stationary IBI time series. The main finding is that heart rate time series from infants with sepsis show a breakdown of the long-range correlation behaviour and consequently complexity-loss. The longrange scaling exponents for the sepsis cases showed a statistically significant deviation (p<0.001) from the long-range scaling exponents of the healthy cases. This method may be of use in distinguishing healthy from pathologic time series of infants with sepsis based on differences in the fractal scaling property, and could be introduced into the clinical practice for the assessment of heart rate patterns of new born infants.