Complex systems and the technology of variability analysis

Serial heart rhythm complexity changes in patients with anterior wall ST segment elevation myocardial infarction

Heart rhythm complexity analysis has been shown to have good prognostic power in patients with cardiovascular disease. The aim of this study was to analyze serial changes in heart rhythm complexity from the acute to chronic phase of acute myocardial infarction (MI). We prospectively enrolled 27 patients with anterior wall ST segment elevation myocardial infarction (STEMI) and 42 control subjects. In detrended fluctuation analysis (DFA), the patients had significantly lower DFAα2 in the acute stage (within 72 hours) and lower DFAα1 at 3 months and 12 months after MI. In multiscale entropy (MSE) analysis, the patients had a lower slope 5 in the acute stage, which then gradually increased during the follow-up period. The areas under the MSE curves for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were lower throughout the chronic stage. Area 6–20 had the greatest discriminatory power to differentiate the post-MI patients (at 1 year) from the controls. In both the net reclassification improvement and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of the linear parameters to differentiate the post-MI patients from the controls. In conclusion, the patients with STEMI had serial changes in cardiac complexity.

Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies

OBJECTIVE

To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015).

DATA SOURCES

Literature review, research data, and expert opinion.

STUDY SELECTION

Not applicable.

DATA EXTRACTION

Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature.

DATA SYNTHESIS

Summary of presentations and discussion supported and supplemented by relevant literature.

CONCLUSIONS

There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.

Metabolic Mechanisms of Stress Hyperglycemia

Stress hyperglycemia has gained the attention of virtually every physician who encounters critically ill patients, with the emergence of clinical data supporting tight glycemic control and intensive insulinization for optimal outcome. In order to effectively manage stress hyperglycemia, newer theories of critical illness and the interactions of the brain, neuroendocrine axis, and immune system need to be explored. Nonlinear physiologic processes, glucose allostasis, immune-neuroendocrine axis activation, and molecular mechanisms of insulin receptor signal transduction contribute to a novel model of stress hyperglycemia. In chronic critical illness, allostatic overload leads to a plurality of organ-system derangements and eventually death. Intervention not only involves insulinization according to neurofuzzy logic but also targeting more proximate events with cognitive/behavioral therapy and hypothalamic releasing factors.

Sleep Assessment in Large Cohort Studies with High-Resolution Accelerometers

Accelerometry can be a practical replacement for polysomnography in large observational studies. This review discusses the need for sleep characterization in large observational studies, exemplified by the practices of the ongoing German National Cohort study. After brief descriptions of the physical principles and state-of-the-art accelerometer devices and an overview of public data analysis algorithms for sleep-wake differentiation, we demonstrate that the spectral properties of acceleration data provide additional features that can be exploited. This leads to a periodogram-based sleep detection algorithm. Finally, we address issues of data handling and quality assurance in large cohort studies.

Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back

Advancing from two core traits of biological systems: multilevel network organization and nonlinearity, we review a host of novel and readily available techniques to explore and analyze their complex dynamic behavior within the framework of experimental-computational synergy. In the context of concrete biological examples, analytical methods such as wavelet, power spectra, and metabolomics-fluxomics analyses, are presented, discussed, and their strengths and limitations highlighted. Further shown is how time series from stationary and nonstationary biological variables and signals, such as membrane potential, high-throughput metabolomics, O₂ and CO₂ levels, bird locomotion, at the molecular, (sub)cellular, tissue, and whole organ and animal levels, can reveal important information on the properties of the underlying biological networks. Systems biology-inspired computational methods start to pave the way for addressing the integrated functional dynamics of metabolic, organelle and organ networks. As our capacity to unravel the control and regulatory properties of these networks and their dynamics under normal or pathological conditions broadens, so is our ability to address endogenous rhythms and clocks to improve health-span in human aging, and to manage complex metabolic disorders, neurodegeneration, and cancer. WIREs Syst Biol Med 2017, 9:e1352. doi: 10.1002/wsbm.1352 For further resources related to this article, please visit the WIREs website.

Electroencephalogram variability in patients with cirrhosis associates with the presence and severity of hepatic encephalopathy

BACKGROUND & AIMS

The outputs of physiological systems fluctuate in a complex manner even under resting conditions. Decreased variability or increased regularity of these outputs is documented in several disease states. Changes are observed in the spatial and temporal configuration of the electroencephalogram (EEG) in patients with hepatic encephalopathy (HE), but there is no information on the variability of the EEG signal in this condition. The aim of this study was to measure and characterize EEG variability in patients with cirrhosis and to determine its relationship to neuropsychiatric status.

METHODS

Eyes-closed, awake EEGs were obtained from 226 patients with cirrhosis, classified, using clinical and psychometric criteria, as neuropsychiatrically unimpaired (n=127) or as having minimal (n=21) or overt (n=78) HE, and from a reference population of 137 healthy controls. Analysis of EEG signal variability was undertaken using continuous wavelet transform and sample entropy.

RESULTS

EEG variability was reduced in the patients with cirrhosis compared with the reference population (coefficient of variation: 21.2% [19.3-23.4] vs. 22.4% [20.8-24.5]; p<0.001). A significant association was observed between EEG variability and neuropsychiatric status; thus, variability was increased in the patients with minimal HE compared with their neuropsychiatrically unimpaired counterparts (sample entropy: 0.98 [0.87-1.14] vs. 0.83 [0.75-0.95]; p=0.02), and compared with the patients with overt HE (sample entropy: 0.98 [0.87-1.14] vs. 0.82 [0.71-1.01]; p=0.01).

CONCLUSIONS

Variability of the EEG is associated with both the presence and severity of HE. This novel finding may provide new insights into the pathophysiology of HE and provide a means for monitoring patients over time.

LAY SUMMARY

Decreased variability or increased regularity of physiological systems is documented in several disease states. Variability of the electroencephalogram was found to be associated with both the presence and severity of brain dysfunction in patients with chronic liver disease.

Heart rate variability during high heat stress: a comparison between young and older adults with and without Type 2 diabetes

We examined whether older individuals with and without Type 2 diabetes (T2D) experience differences in heart rate variability (HRV) during a 3-h exposure to high heat stress compared with young adults. Young (Young; n = 22; 23 ± 3 yr) and older individuals with (T2D; n = 11; 59 ± 9 yr) and without (Older; n = 25; 63 ± 5 yr) T2D were exposed to heat stress (44°C, 30% relative humidity) for 3 h. Fifty-five HRV measures were assessed for 15 min at baseline and at minutes 82.5-97.5 (Mid) and minutes 165-180 (End) during heat stress. When compared with Young, a similar number of HRV indices were significantly different (P < 0.05) in Older (Baseline: 35; Mid: 29; End: 32) and T2D (Baseline: 31; Mid: 30; End: 27). In contrast, the number of HRV indices significantly different (P < 0.05) between Older and T2D were far fewer (Baseline: 13, Mid: 1, End: 3). Within-group analyses demonstrated a greater change in the Young group's HRV during heat stress compared with Older and T2D; the number of significantly different (P < 0.05) HRV indices between baseline and End were 42, 29, and 20, for Young, Older, and T2D, respectively. Analysis of specific HRV domains suggest that the Young group experienced greater sympathetic activity during heat stress compared with Older and T2D. In conclusion, when compared with young, older individuals with and without T2D demonstrate low HRV at baseline and less change in HRV (including an attenuated sympathetic response) during 3 h high heat stress, potentially contributing to impaired thermoregulatory function.

Heart rhythm complexity impairment in patients undergoing peritoneal dialysis

Cardiovascular disease is one of the leading causes of death in patients with advanced renal disease. The objective of this study was to investigate impairments in heart rhythm complexity in patients with end-stage renal disease. We prospectively analyzed 65 patients undergoing peritoneal dialysis (PD) without prior cardiovascular disease and 72 individuals with normal renal function as the control group. Heart rhythm analysis including complexity analysis by including detrended fractal analysis (DFA) and multiscale entropy (MSE) were performed. In linear analysis, the PD patients had a significantly lower standard deviation of normal RR intervals (SDRR) and percentage of absolute differences in normal RR intervals greater than 20 ms (pNN20). Of the nonlinear analysis indicators, scale 5, area under the MSE curve for scale 1 to 5 (area 1–5) and 6 to 20 (area 6–20) were significantly lower than those in the control group. In DFA anaylsis, both DFA α1 and DFA α2 were comparable in both groups. In receiver operating characteristic curve analysis, scale 5 had the greatest discriminatory power for two groups. In both net reclassification improvement model and integrated discrimination improvement models, MSE parameters significantly improved the discriminatory power of SDRR, pNN20, and pNN50. In conclusion, PD patients had worse cardiac complexity parameters. MSE parameters are useful to discriminate PD patients from patients with normal renal function.

Burn trauma disrupts circadian rhythms in rat liver

Circadian rhythms play an important role in maintaining homeostasis and solid organ function. The purpose of this study is to assess the implications of burn injury in rats on the underlying circadian patterns of gene expression in liver. Circadian-regulated genes and burn-induced genes were identified by applying consensus clustering methodology to temporally differentially expressed probe sets obtained from burn and sham-burn data sets. Of the liver specific genes which we hypothesize that exhibit circadian rhythmicity, 88% are not differentially expressed following the burn injury. Specifically, the vast majority of the circadian regulated-genes representing central carbon and nitrogen metabolism are "up-regulated" after the burn injury, indicating the onset of hypermetabolism. In addition, cell-cell junction and membrane structure related genes showing rhythmic behavior in the control group were not differentially expressed across time in the burn group, which could be an indication of hepatic damage due to the burn. Finally, the suppression of the immune function related genes is observed in the postburn phase, implying the severe "immunosuppression". Our results demonstrated that the short term response (24-h post injury) manifests a loss of circadian variability possibly compromising the host in terms of subsequent challenges.

Multiscale entropy analysis of heart rate variability in heart failure, hypertensive, and sinoaortic-denervated rats: classical and refined approaches

The analysis of heart rate variability (HRV) by nonlinear methods has been gaining increasing interest due to their ability to quantify the complexity of cardiovascular regulation. In this study, multiscale entropy (MSE) and refined MSE (RMSE) were applied to track the complexity of HRV as a function of time scale in three pathological conscious animal models: rats with heart failure (HF), spontaneously hypertensive rats (SHR), and rats with sinoaortic denervation (SAD). Results showed that HF did not change HRV complexity, although there was a tendency to decrease the entropy in HF animals. On the other hand, SHR group was characterized by reduced complexity at long time scales, whereas SAD animals exhibited a smaller short- and long-term irregularity. We propose that short time scales (1 to 4), accounting for fast oscillations, are more related to vagal and respiratory control, whereas long time scales (5 to 20), accounting for slow oscillations, are more related to sympathetic control. The increased sympathetic modulation is probably the main reason for the lower entropy observed at high scales for both SHR and SAD groups, acting as a negative factor for the cardiovascular complexity. This study highlights the contribution of the multiscale complexity analysis of HRV for understanding the physiological mechanisms involved in cardiovascular regulation.

Immediate effect of basic body awareness therapy on heart rate variability

OBJECTIVE

To determine the immediate effect of a Basic Body Awareness Therapy (BAT) session on measures of heart rate variability (HRV) in healthy young people.

METHODS

13 healthy young subjects of both genders, who showed no illnesses related to the autonomic nervous system (ANS) underwent an ANS evaluation before and after conducting a session of BAT. The assessment of ANS activity was conducted through the HR with the aid of Nerve-Express(®) software. The BAT session lasted for 50 min and was performed by one investigator (blinded to the assessment procedures).

RESULTS

After BAT session significant improvement was found in the sympathetic and parasympathetic modulation (p < 0.05), and the general estimate of heart rate variability (p < 0.04).

CONCLUSION

BAT was found to be an effective, easy to apply and inexpensive therapeutic technique, able to change ANS in order to improve HR which may suggest better health conditions for participating individuals.

Detrended Fluctuation Analysis of Heart Rate Dynamics Is an Important Prognostic Factor in Patients with End-Stage Renal Disease Receiving Peritoneal Dialysis

BACKGROUND AND OBJECTIVES

Patients with severe kidney function impairment often have autonomic dysfunction, which could be evaluated noninvasively by heart rate variability (HRV) analysis. Nonlinear HRV parameters such as detrended fluctuation analysis (DFA) has been demonstrated to be an important outcome predictor in patients with cardiovascular diseases. Whether cardiac autonomic dysfunction measured by DFA is also a useful prognostic factor in patients with end-stage renal disease (ESRD) receiving peritoneal dialysis (PD) remains unclear. The purpose of the present study was designed to test the hypothesis.

MATERIALS AND METHODS

Patients with ESRD receiving PD were included for the study. Twenty-four hour Holter monitor was obtained from each patient together with other important traditional prognostic makers such as underlying diseases, left ventricular ejection fraction (LVEF) and serum biochemistry profiles. Short-term (DFAα1) and long-term (DFAα2) DFA as well as other linear HRV parameters were calculated.

RESULTS

A total of 132 patients (62 men, 72 women) with a mean age of 53.7±12.5 years were recruited from July 2007 to March 2009. During a median follow-up period of around 34 months, eight cardiac and six non-cardiac deaths were observed. Competing risk analysis demonstrated that decreased DFAα1 was a strong prognostic predictor for increased cardiac and total mortality. ROC analysis showed that the AUC of DFAα1 (<0.95) to predict mortality was 0.761 (95% confidence interval (CI). = 0.617-0.905). DFAα1≧ 0.95 was associated with lower cardiac mortality (Hazard ratio (HR) 0.062, 95% CI = 0.007-0.571, P = 0.014) and total mortality (HR = 0.109, 95% CI = 0.033-0.362, P = 0.0003).

CONCLUSION

Cardiac autonomic dysfunction evaluated by DFAα1 is an independent predictor for cardiac and total mortality in patients with ESRD receiving PD.

Novel application of multi dynamic trend analysis as a sensitive tool for detecting the effects of aging and congestive heart failure on heart rate variability

The complex fluctuations in heart rate variability (HRV) reflect cardiac autonomic modulation and are an indicator of congestive heart failure (CHF). This paper proposes a novel nonlinear approach to HRV investigation, the multi dynamic trend analysis (MDTA) method, based on the empirical mode decomposition algorithm of the Hilbert-Huang transform combined with a variable-sized sliding-window method. Electrocardiographic signal data obtained from the PhysioNet database were used. These data were from subjects with CHF (mean age = 59.4 ± 8.4), an age-matched elderly healthy control group (59.3 ± 10.6), and a healthy young group (30.3 ± 4.8); the HRVs of these subjects were processed using the MDTA method, time domain analysis, and frequency domain analysis. Among all HRV parameters, the MDTA absolute value slope (MDTS) and MDTA deviation (MDTD) exhibited the greatest area under the curve (AUC) of the receiver operating characteristics in distinguishing between the CHF group and the healthy controls (AUC = 1.000) and between the healthy elderly subject group and the young subject group (AUC = 0.834 ± 0.067 for MDTS; 0.837 ± 0.066 for MDTD). The CHF subjects presented with lower MDTA indices than those of the healthy elderly subject group. Furthermore, the healthy elderly subjects exhibited lower MDTA indices than those of the young controls. The MDTA method can adaptively and automatically identify the intrinsic fluctuation on variable temporal and spatial scales when investigating complex fluctuations in the cardiac autonomic regulation effects of aging and CHF.

Diminished autonomic neurocardiac function in patients with generalized anxiety disorder

BACKGROUND

Generalized anxiety disorder (GAD) is a chronic and highly prevalent disorder that is characterized by a number of autonomic nervous system symptoms. The purpose of this study was to investigate the linear and nonlinear complexity measures of heart rate variability (HRV), measuring autonomic regulation, and to evaluate the relationship between HRV parameters and the severity of anxiety, in medication-free patients with GAD.

METHODS

Assessments of linear and nonlinear complexity measures of HRV were performed in 42 medication-free patients with GAD and 50 healthy control subjects. In addition, the severity of anxiety symptoms was assessed using the State-Trait Anxiety Inventory and Beck Anxiety Inventory. The values of the HRV measures of the groups were compared, and the correlations between the HRV measures and the severity of anxiety symptoms were assessed.

RESULTS

The GAD group showed significantly lower standard deviation of RR intervals and the square root of the mean squared differences of successive normal sinus intervals values compared to the control group (P<0.01). The approximate entropy value, which is a nonlinear complexity indicator, was also significantly lower in the patient group than in the control group (P<0.01). In correlation analysis, there were no significant correlations between HRV parameters and the severity of anxiety symptoms.

CONCLUSION

The present study indicates that GAD is significantly associated with reduced HRV, suggesting that autonomic neurocardiac integrity is substantially impaired in patients with GAD. Future prospective studies are required to investigate the effects of pharmacological or non-pharmacological treatment on neuroautonomic modulation in patients with GAD.

Glycemic variability during algorithmic titration of insulin among hospitalized patients with type 2 diabetes and heart failure

AIMS

The objective of this study is to assess hypoglycemia and glycemic variability (GV) in hospitalized patients with and without heart failure (HF) exacerbation.

METHODS

Hospitalized patients with type 2 diabetes (T2D) with (N=35) or without (N=16) HF who had hyperglycemia or significant insulin use were included. Subjects underwent continuous glucose monitoring during algorithmic titration of basal bolus insulin.

RESULTS

HF subjects had lower glucose coefficient of variation ([CV], 31±12 vs. 22±8.2, p=0.02), lower Low Blood Glucose Index (LBGI) and less hypoglycemia (25% vs. 2.6%, p=0.02), but similar mean glucose and glycemic lability index as non-HF subjects on day 1, but not on day 2. Sensor CV was correlated with hypoglycemia (ρ 0.32, p=0.02), HF status (ρ -0.35, p=0.013), T2D duration (ρ 0.29, p=0.04), insulin use prior to admission (ρ 0.42, p=0.002) and catecholamine levels. After controlling for differences in age, HbA1c, hypoglycemia, catecholamine levels, QT interval, and beta blocker use, only HF and diabetes duration or insulin use prior to admission were independent predictors of CV. HF had less robust associations with LBGI in multivariable models.

CONCLUSIONS

HF is not associated with increased GV or hypoglycemia risk during initial titration of insulin. Further research is needed to determine prognostic implications.

Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest

Early cardiopulmonary resuscitation together with early defibrillation is a key point in the chain of survival for cardiac arrest. Optimizing the timing of defibrillation by predicting the possibility of successful electric shock can guide treatments between defibrillation and cardiopulmonary resuscitation and improve the rate of restoration of spontaneous circulation. Numerous methods have been proposed for predicting defibrillation success based on quantification of the ventricular fibrillation waveform during past decades. To date, however, no analytical technique has been widely accepted for clinical application. In the present study, we investigate whether median stepping increment that is calculated from the Euclidean distance of consecutive points in Poincare plot could be used to predict the likelihood of successful defibrillation. Electrocardiographic recordings of out-of-hospital cardiac arrest patients were obtained from the external defibrillators. The performance of the proposed method was evaluated by receiver operating characteristic curve and compared with the results of other established features. The results indicated that median stepping increment has comparable performance to the established methods in predicting the likelihood of successful defibrillation.

Does heart rate variability reflect the systemic inflammatory response in a fetal sheep model of lipopolysaccharide-induced sepsis?

Fetal inflammatory response occurs during chorioamnionitis, a frequent and often subclinical inflammation associated with increased risk for brain injury and life-lasting neurologic deficits. No means of early detection exist. We hypothesized that systemic fetal inflammation without septic shock will be reflected in alterations of fetal heart rate (FHR) variability (fHRV) distinguishing baseline versus inflammatory response states. In chronically instrumented near-term fetal sheep (n = 24), we induced an inflammatory response with lipopolysaccharide (LPS) injected intravenously (n = 14). Ten additional fetuses served as controls. We measured fetal plasma inflammatory cytokine IL-6 at baseline, 1, 3, 6, 24 and 48 h. 44 fHRV measures were determined continuously every 5 min using continuous individualized multi-organ variability analysis (CIMVA). CIMVA creates an fHRV measures matrix across five signal-analytical domains, thus describing complementary properties of fHRV. Using principal component analysis (PCA), a widely used technique for dimensionality reduction, we derived and quantitatively compared the CIMVA fHRV PCA signatures of inflammatory response in LPS and control groups. In the LPS group, IL-6 peaked at 3 h. In parallel, PCA-derived fHRV composite measures revealed a significant difference between LPS and control group at different time points. For the LPS group, a sharp increase compared to baseline levels was observed between 3 h and 6 h, and then abating to baseline levels, thus tracking closely the IL-6 inflammatory profile. This pattern was not observed in the control group. We also show that a preselection of fHRV measures prior to the PCA can potentially increase the difference between LPS and control groups, as early as 1 h post LPS injection. We propose a fHRV composite measure that correlates well with levels of inflammation and tracks well its temporal profile. Our results highlight the potential role of HRV to study and monitor the inflammatory response non-invasively over time.

Reversible heart rhythm complexity impairment in patients with primary aldosteronism

Excess aldosterone secretion in patients with primary aldosteronism (PA) impairs their cardiovascular system. Heart rhythm complexity analysis, derived from heart rate variability (HRV), is a powerful tool to quantify the complex regulatory dynamics of human physiology. We prospectively analyzed 20 patients with aldosterone producing adenoma (APA) that underwent adrenalectomy and 25 patients with essential hypertension (EH). The heart rate data were analyzed by conventional HRV and heart rhythm complexity analysis including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We found APA patients had significantly decreased DFAα2 on DFA analysis and decreased area 1-5, area 6-15, and area 6-20 on MSE analysis (all p < 0.05). Area 1-5, area 6-15, area 6-20 in the MSE study correlated significantly with log-transformed renin activity and log-transformed aldosterone-renin ratio (all p < = 0.01). The conventional HRV parameters were comparable between PA and EH patients. After adrenalectomy, all the altered DFA and MSE parameters improved significantly (all p < 0.05). The conventional HRV parameters did not change. Our result suggested that heart rhythm complexity is impaired in APA patients and this is at least partially reversed by adrenalectomy.

The trajectory of life. Decreasing physiological network complexity through changing fractal patterns

In this position paper, we submit a synthesis of theoretical models based on physiology, non-equilibrium thermodynamics, and non-linear time-series analysis. Based on an understanding of the human organism as a system of interconnected complex adaptive systems, we seek to examine the relationship between health, complexity, variability, and entropy production, as it might be useful to help understand aging, and improve care for patients. We observe the trajectory of life is characterized by the growth, plateauing and subsequent loss of adaptive function of organ systems, associated with loss of functioning and coordination of systems. Understanding development and aging requires the examination of interdependence among these organ systems. Increasing evidence suggests network interconnectedness and complexity can be captured/measured/associated with the degree and complexity of healthy biologic rhythm variability (e.g., heart and respiratory rate variability). We review physiological mechanisms linking the omics, arousal/stress systems, immune function, and mitochondrial bioenergetics; highlighting their interdependence in normal physiological function and aging. We argue that aging, known to be characterized by a loss of variability, is manifested at multiple scales, within functional units at the small scale, and reflected by diagnostic features at the larger scale. While still controversial and under investigation, it appears conceivable that the integrity of whole body complexity may be, at least partially, reflected in the degree and variability of intrinsic biologic rhythms, which we believe are related to overall system complexity that may be a defining feature of health and it's loss through aging. Harnessing this information for the development of therapeutic and preventative strategies may hold an opportunity to significantly improve the health of our patients across the trajectory of life.

Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.

Repeatability of Respiratory Exchange Ratio Time Series Analysis

Currently, there are few studies on the repeatability of a time series analysis of respiratory exchange ratio (RER) under the same conditions. This repeated-measures study compared 2 trials completed under the same conditions. After an 8-hour fast, subjects (7 male and 5 female) (mean ± SD) of age 27.3 ± 3.7 years, body weight of 71.8 ± 8.4 kg, percent body fat of 16.4 ± 8.1%, and peak oxygen uptake (V[Combining Dot Above]O2peak) of 46.0 ± 5.3 ml·kg·min completed a V[Combining Dot Above]O2peak test followed 7 days later by a cycle ergometer test at 30% of ventilatory threshold (VT) and 60% of VT for 15 minutes each. These tests were repeated again 7 days later. Paired t-tests revealed no significant differences between the tests for mean RER or sample entropy (SampEn) score at both intensities. The coefficients of variance were generally similar for the mean and SampEn of the RER. The intraclass correlation coefficient (ICC) values for the mean RER at 30% of VT were 1.00 and at 60% of VT were 0.92. The ICC values for the SampEn RER at 30% of VT were 0.81 and at 60% of VT were the lowest at 0.25. Bland-Altman plots demonstrated a measure of agreement between both methods. We demonstrated that RER measurements at 30 and 60% of VT are repeatable during steady-state cycle ergometery. Future research should determine if this finding is consistent with a larger sample size and different exercise intensities.

Variations in cognitive demand affect heart rate in typically developing children and children at risk for developmental coordination disorder

BACKGROUND

Developmental coordination disorder (DCD) is a diagnosis for children who present with movement difficulties, but are of normal intelligence without neurological deficits. Previous studies have demonstrated that children with DCD exhibit perceptual deficits and lower cognition performance. To date, their autonomic nervous system (ANS) responses during tasks requiring cognitive and perceptual effort have not been compared to typically developing children (TDC).

OBJECTIVE

The present study investigated heart rate variability (HRV) as a marker for ANS response differences between DCD and TDC, and the impact of different levels of task difficulty.

METHODS

Participants were 60 individuals (9-10 years); 30 children at risk for DCD, and 30 TDC. Each participant performed two tasks each of which demanded enhanced cognitive effort: a visual signal detection task and a digit memory task-each task had two levels of difficulty, low (LD) and high (HD). Heart rate responses were continuously recorded during performance of each task. Frequency domain analysis and heart rate sample entropy (SampEn) were computed to determine ANS responses in each of the tasks.

RESULTS

HRV differences were detected between the two levels of task difficulty, LD and HD, for the visual signal detection task, but not for the digit memory task. HRV differences between LD and HD conditions were greater for TDC children than DCD when engaged in visual signal detection task, compare to the memory task.

INTERPRETATION

The results suggest that children at risk for DCD may show decreased HRV as a marker for altered ANS responses and potential deficits in the linkage between their perceptions and actions.

Cardiac autonomic changes after 40 hours of total sleep deprivation in women

OBJECTIVES

The effect of total sleep deprivation on heart rate variability (HRV) in groups of postmenopausal women on oral hormone therapy (HT) (on-HT, n = 10, 64.2 (1.4) years), postmenopausal women without HT (off-HT, n = 10, 64.6 (1.4) years) and young women (n = 11, 23.1 (0.5) years) was studied using a prospective case-control setup.

METHODS

Polysomnography was performed over an adaptation night, a baseline night, and a recovery night after 40 h of total sleep deprivation. Time and frequency domain and nonlinear HRV from overnight electrocardiogram recordings were compared between groups during baseline and recovery nights. Further, the changes in HRV from baseline to recovery were analysed and compared between groups. Finally, correlations of HRV to percentages of sleep stages and measures of sleep fragmentation were analysed during baseline and recovery.

RESULTS

Young women had higher HRV than older women; the most marked difference was between young and on-HT postmenopausal women. Sleep deprivation induced a decrease in frequency domain HRV in young and in off-HT women, an increase in α2 in off-HT women, and an increase in mean heart rate in on-HT women. The sleep deprivation effect was mainly uncorrelated to changes in sleep parameters.

CONCLUSIONS

Acute total sleep deprivation has a deleterious effect on the autonomic nervous system in young women, but an even more pronounced effect in postmenopausal women. Hormone therapy use in late postmenopause does not give protection against these changes. These harmful effects may partly explain the increased cardiovascular morbidity and overall mortality associated with sleep loss.

Heart rate complexity in sinoaortic-denervated mice

NEW FINDINGS

What is the central question of this study? New measurements for cardiovascular complexity, such as detrended fluctuation analysis (DFA) and multiscale entropy (MSE), have been shown to predict cardiovascular outcomes. Given that cardiovascular diseases are accompanied by autonomic imbalance and decreased baroreflex sensitivity, the central question is: do baroreceptors contribute to cardiovascular complexity? What is the main finding and its importance? Sinoaortic denervation altered both DFA scaling exponents and MSE, indicating that both short- and long-term mechanisms of complexity are altered in sinoaortic denervated mice, resulting in a loss of physiological complexity. These results suggest that the baroreflex is a key element in the complex structures involved in heart rate variability regulation. Recently, heart rate (HR) oscillations have been recognized as complex behaviours derived from non-linear processes. Physiological complexity theory is based on the idea that healthy systems present high complexity, i.e. non-linear, fractal variability at multiple scales, with long-range correlations. The loss of complexity in heart rate variability (HRV) has been shown to predict adverse cardiovascular outcomes. Based on the idea that most cardiovascular diseases are accompanied by autonomic imbalance and a decrease in baroreflex sensitivity, we hypothesize that the baroreflex plays an important role in complex cardiovascular behaviour. Mice that had been subjected to sinoaortic denervation (SAD) were implanted with catheters in the femoral artery and jugular vein 5 days prior to the experiment. After recording the baseline arterial pressure (AP), pulse interval time series were generated from the intervals between consecutive values of diastolic pressure. The complexity of the HRV was determined using detrended fluctuation analysis and multiscale entropy. The detrended fluctuation analysis α1 scaling exponent (a short-term index) was remarkably decreased in the SAD mice (0.79 ± 0.06 versus 1.13 ± 0.04 for the control mice), whereas SAD slightly increased the α2 scaling exponent (a long-term index; 1.12 ± 0.03 versus 1.04 ± 0.02 for control mice). In the SAD mice, the total multiscale entropy was decreased (13.2 ± 1.3) compared with the control mice (18.9 ± 1.4). In conclusion, fractal and regularity structures of HRV are altered in SAD mice, affecting both short- and long-term mechanisms of complexity, suggesting that the baroreceptors play a considerable role in the complex structure of HRV.

All this monitoring…what's necessary, what's not?

The goal of perioperative monitoring is to aid the clinician in optimizing care to achieve the best possible survival with the lowest possible morbidity. Ideally, we would like to have monitoring that can rapidly and accurately identify perturbations in circulatory well-being that would permit timely intervention and allow for restoration before the patient is damaged. The evidence to support the use of our standard monitoring strategies (continuous electrocardiography, blood pressure, central venous pressure, oxygen saturation and capnography) is based on expert opinion, case series, or at best observational studies. While these monitoring parameters will identify life-threatening events, they provide no direct information concerning the oxygen economy of the patient. Nevertheless, they are mandated by professional societies representing specialists in cardiac disease, critical care, and anesthesiology. Additional non-routine monitoring strategies that provide data concerning the body's oxygen economy, such as venous saturation monitoring and near infrared spectroscopy, have shown promise in prospective observational studies in managing these complex groups of patients. Ideally, high-level evidence would be required before adopting these newer strategies, but in the absence of new funding sources and the challenges of the wide variation in practice patterns between centers, this seems unlikely. The evidence supporting the current standard perioperative monitoring strategies will be reviewed. In addition, evidence supporting non-routine monitoring strategies will be reviewed and their potential for added benefit assessed.

Multi-scale symbolic entropy analysis provides prognostic prediction in patients receiving extracorporeal life support

INTRODUCTION

Extracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving ECLS. The primary outcome is death or urgent transplantation during the index admission.

METHODS

Fifty-seven patients receiving ECLS less than 24 hours and 23 control subjects were enrolled. Digital 24-hour Holter electrocardiograms were recorded and three MSsE parameters (slope 5, Area 6-20, Area 6-40) associated with the multiscale correlation and complexity of heart beat fluctuation were calculated.

RESULTS

Patients receiving ECLS had significantly lower value of slope 5, area 6 to 20, and area 6 to 40 than control subjects. During the follow-up period, 29 patients met primary outcome. Age, slope 5, Area 6 to 20, Area 6 to 40, acute physiology and chronic health evaluation II score, multiple organ dysfunction score (MODS), logistic organ dysfunction score (LODS), and myocardial infarction history were significantly associated with primary outcome. Slope 5 showed the greatest discriminatory power. In a net reclassification improvement model, slope 5 significantly improved the predictive power of LODS; Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in MODS. In an integrated discrimination improvement model, slope 5 added significantly to the prediction power of each clinical parameter. Area 6 to 20 and Area 6 to 40 significantly improved the predictive power in sequential organ failure assessment.

CONCLUSIONS

MSsE provides additional prognostic information in patients receiving ECLS.

[Analysis of heart rate variability. Mathematical description and practical application]

The analysis of heart rate variability (HRV) has recently become established as a non-invasive measurement for estimation of demands on the cardiovascular system. The HRV reflects the interaction of the sympathetic and parasympathetic nervous systems and allows the influence of the autonomic nervous system on the regulation of the cardiovascular system to be mathematically described. This review explicates the analysis method of HRV for time, frequency and non-linear methods as well as the range of parameters and the demand on acquisition time. The necessity and possibilities of artefact correction and advice for the selection of a reasonable acquisition period are discussed and standard values for selected HRV parameters are presented.

From beat rate variability in induced pluripotent stem cell-derived pacemaker cells to heart rate variability in human subjects

BACKGROUND

We previously reported that induced pluripotent stem cell-derived cardiomyocytes manifest beat rate variability (BRV) resembling heart rate variability (HRV) in the human sinoatrial node. We now hypothesized the BRV-HRV continuum originates in pacemaker cells.

OBJECTIVE

To investigate whether cellular BRV is a source of HRV dynamics, we hypothesized 3 levels of interaction among different cardiomyocyte entities: (1) single pacemaker cells, (2) networks of electrically coupled pacemaker cells, and (3) the in situ sinoatrial node.

METHODS

We measured BRV/HRV properties in single pacemaker cells, induced pluripotent stem cell-derived contracting embryoid bodies (EBs), and electrocardiograms from the same individual.

RESULTS

Pronounced BRV/HRV was present at all 3 levels. The coefficient of variance of interbeat intervals and Poincaré plot indices SD1 and SD2 for single cells were 20 times greater than those for EBs (P < .05) and the in situ heart (the latter two were similar; P > .05). We also compared BRV magnitude among single cells, small EBs (~5-10 cells), and larger EBs (>10 cells): BRV indices progressively increased with the decrease in the cell number (P < .05). Disrupting intracellular Ca(2+) handling markedly augmented BRV magnitude, revealing a unique bimodal firing pattern, suggesting that intracellular mechanisms contribute to BRV/HRV and the fractal behavior of heart rhythm.

CONCLUSION

The decreased BRV magnitude in transitioning from the single cell to the EB suggests that the HRV of in situ hearts originates from the summation and integration of multiple cell-based oscillators. Hence, complex interactions among multiple pacemaker cells and intracellular Ca(2+) handling determine HRV in humans and cardiomyocyte networks.

Classification of heart rate signals of healthy and pathological subjects using threshold based symbolic entropy

The dynamical fluctuations of biological signals provide a unique window to construe the underlying mechanism of the biological systems in health and disease. Recent research evidences suggest that a wide class of diseases appear to degrade the biological complexity and adaptive capacity of the system. Heart rate signals are one of the most important biological signals that have widely been investigated during the last two and half decades. Recent studies suggested that heart rate signals fluctuate in a complex manner. Various entropy based complexity analysis measures have been developed for quantifying the valuable information that may be helpful for clinical monitoring and for early intervention. This study is focused on determining HRV dynamics to distinguish healthy subjects from patients with certain cardiac problems using symbolic time series analysis technique. For that purpose, we have employed recently developed threshold based symbolic entropy to cardiac inter-beat interval time series of healthy, congestive heart failure and atrial fibrillation subjects. Normalized Corrected Shannon Entropy (NCSE) was used to quantify the dynamics of heart rate signals by continuously varying threshold values. A rule based classifier was implemented for classification of different groups by selecting threshold values for the optimal separation. The findings indicated that there is reduction in the complexity of pathological subjects as compared to healthy ones at wide range of threshold values. The results also demonstrated that complexity decreased with disease severity.

Phase synchronization in electroencephalographic recordings prognosticates outcome in paediatric coma

Brain injury from trauma, cardiac arrest or stroke is the most important cause of death and acquired disability in the paediatric population. Due to the lifetime impact of brain injury, there is a need for methods to stratify patient risk and ultimately predict outcome. Early prognosis is fundamental to the implementation of interventions to improve recovery, but no clinical model as yet exists. Healthy physiology is associated with a relative high variability of physiologic signals in organ systems. This was first evaluated in heart rate variability research. Brain variability can be quantified through electroencephalographic (EEG) phase synchrony. We hypothesised that variability in brain signals from EEG recordings would correlate with patient outcome after brain injury. Lower variability in EEG phase synchronization, would be associated with poor patient prognosis. A retrospective study, spanning 10 years (2000-2010) analysed the scalp EEGs of children aged 1 month to 17 years in coma (Glasgow Coma Scale, GCS, <8) admitted to the paediatric critical care unit (PCCU) following brain injury from TBI, cardiac arrest or stroke. Phase synchrony of the EEGs was evaluated using the Hilbert transform and the variability of the phase synchrony calculated. Outcome was evaluated using the 6 point Paediatric Performance Category Score (PCPC) based on chart review at the time of hospital discharge. Outcome was dichotomized to good outcome (PCPC score 1 to 3) and poor outcome (PCPC score 4 to 6). Children who had a poor outcome following brain injury secondary to cardiac arrest, TBI or stroke, had a higher magnitude of synchrony (R index), a lower spatial complexity of the synchrony patterns and a lower temporal variability of the synchrony index values at 15 Hz when compared to those patients with a good outcome.

Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

Introduction

Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown.

Methods

We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models.

Results

Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation failure of 3.0 (95% confidence interval (CI) 1.2 to 5.2) and 3.5 (95% CI 1.9 to 5.4), respectively.

Conclusions

Altered HRV and RRV (during the SBT prior to extubation) are significantly associated with extubation failure. A predictive model using RRV during the last SBT provided optimal accuracy of prediction in all patients, with improved accuracy when combined with clinical impression or RSBI. This model requires a validation cohort to evaluate accuracy and generalizability.

Trial registration

ClinicalTrials.gov NCT01237886. Registered 13 October 2010.

Actigraphic assessment of motor activity in acutely admitted inpatients with bipolar disorder

Introduction

Mania is associated with increased activity, whereas psychomotor retardation is often found in bipolar depression. Actigraphy is a promising tool for monitoring phase shifts and changes following treatment in bipolar disorder. The aim of this study was to compare recordings of motor activity in mania, bipolar depression and healthy controls, using linear and nonlinear analytical methods.

Materials and Methods

Recordings from 18 acutely hospitalized inpatients with mania were compared to 12 recordings from bipolar depression inpatients and 28 healthy controls. 24-hour actigraphy recordings and 64-minute periods of continuous motor activity in the morning and evening were analyzed. Mean activity and several measures of variability and complexity were calculated.

Results

Patients with depression had a lower mean activity level compared to controls, but higher variability shown by increased standard deviation (SD) and root mean square successive difference (RMSSD) over 24 hours and in the active morning period. The patients with mania had lower first lag autocorrelation compared to controls, and Fourier analysis showed higher variance in the high frequency part of the spectrum corresponding to the period from 2–8 minutes. Both patient groups had a higher RMSSD/SD ratio compared to controls. In patients with mania we found an increased complexity of time series in the active morning period, compared to patients with depression. The findings in the patients with mania are similar to previous findings in patients with schizophrenia and healthy individuals treated with a glutamatergic antagonist.

Conclusion

We have found distinctly different activity patterns in hospitalized patients with bipolar disorder in episodes of mania and depression, assessed by actigraphy and analyzed with linear and nonlinear mathematical methods, as well as clear differences between the patients and healthy comparison subjects.

Ventricular arrhythmias are not a short-term reproducible phenomenon--why long recording monitoring is necessary

BACKGROUND

The variability of ventricular arrhythmias (VA) among different days of the week is not well detected by one-day Holter monitoring.

AIMS

To evaluate whether there are differences in VA distribution pattern during long recording period.

METHODS

The EKG was recorded for 14 h per day during 7 days by Holter system in 34 consecutive pat ventricular couplets and non-sustained ventricular tachycardia (NSVT) recording from patients provided graphic data. We applied the Hurst method (H Coefficient) which evaluates whether a repetitive phenomenon is random or not. When the H is >0.5 and <1 means it is not random and implies a long-term memory effect. Considering the arrhythmic variability, the data were also analyzed by repetitive ANOVA comparing incidence of arrhythmias among the days.

RESULTS

Isolated PVCs and ventricular couplets during 98 h recording provided graphic of the occurrence. A trend of increasing and decreasing of arrhythmias was observed which looks erratic. The H coefficient, however, was significantly >0.5 for all patients. Repeated ANOVA showed statistic difference among days in 31 patients with isolated PVCs; in 26 with ventricular couplets and 19 with NSVT when analyzed per hour during week days (p < 0.05).

CONCLUSION

PVCs, ventricular couplets and NSVT are not a random phenomenon. Our data suggest the occurrence of ventricular arrhythmias had no similarity among the days, making unlikely that a single Holter recording for 24h may capture this phenomenon.

Heart rate variability measurement and clinical depression in acute coronary syndrome patients: narrative review of recent literature

AIM

We aimed to explore links between heart rate variability (HRV) and clinical depression in patients with acute coronary syndrome (ACS), through a review of recent clinical research literature.

BACKGROUND

Patients with ACS are at risk for both cardiac autonomic dysfunction and clinical depression. Both conditions can negatively impact the ability to recover from an acute physiological insult, such as unstable angina or myocardial infarction, increasing the risk for adverse cardiovascular outcomes. HRV is recognized as a reflection of autonomic function.

METHODS

A narrative review was undertaken to evaluate state-of-the-art clinical research, using the PubMed database, January 2013. The search terms "heart rate variability" and "depression" were used in conjunction with "acute coronary syndrome", "unstable angina", or "myocardial infarction" to find clinical studies published within the past 10 years related to HRV and clinical depression, in patients with an ACS episode. Studies were included if HRV measurement and depression screening were undertaken during an ACS hospitalization or within 2 months of hospital discharge.

RESULTS

Nine clinical studies met the inclusion criteria. The studies' results indicate that there may be a relationship between abnormal HRV and clinical depression when assessed early after an ACS event, offering the possibility that these risk factors play a modest role in patient outcomes.

CONCLUSION

While a definitive conclusion about the relevance of HRV and clinical depression measurement in ACS patients would be premature, the literature suggests that these measures may provide additional information in risk assessment. Potential avenues for further research are proposed.

Short-term vs. long-term heart rate variability in ischemic cardiomyopathy risk stratification

In industrialized countries with aging populations, heart failure affects 0.3–2% of the general population. The investigation of 24 h-ECG recordings revealed the potential of nonlinear indices of heart rate variability (HRV) for enhanced risk stratification in patients with ischemic heart failure (IHF). However, long-term analyses are time-consuming, expensive, and delay the initial diagnosis. The objective of this study was to investigate whether 30 min short-term HRV analysis is sufficient for comparable risk stratification in IHF in comparison to 24 h-HRV analysis. From 256 IHF patients [221 at low risk (IHF_LR) and 35 at high risk (IHF_HR)] (a) 24 h beat-to-beat time series (b) the first 30 min segment (c) the 30 min most stationary day segment and (d) the 30 min most stationary night segment were investigated. We calculated linear (time and frequency domain) and nonlinear HRV analysis indices. Optimal parameter sets for risk stratification in IHF were determined for 24 h and for each 30 min segment by applying discriminant analysis on significant clinical and non-clinical indices. Long- and short-term HRV indices from frequency domain and particularly from nonlinear dynamics revealed high univariate significances (p < 0.01) discriminating between IHF_LR and IHF_HR. For multivariate risk stratification, optimal mixed parameter sets consisting of 5 indices (clinical and nonlinear) achieved 80.4% AUC (area under the curve of receiver operating characteristics) from 24 h HRV analysis, 84.3% AUC from first 30 min, 82.2 % AUC from daytime 30 min and 81.7% AUC from nighttime 30 min. The optimal parameter set obtained from the first 30 min showed nearly the same classification power when compared to the optimal 24 h-parameter set. As results from stationary daytime and nighttime, 30 min segments indicate that short-term analyses of 30 min may provide at least a comparable risk stratification power in IHF in comparison to a 24 h analysis period.

Complexity and emergent phenomena

Complex biological systems operate under non-equilibrium conditions and exhibit emergent properties associated with correlated spatial and temporal structures. These properties may be individually unpredictable, but tend to be governed by power-law probability distributions and/or correlation. This article reviews the concepts that are invoked in the treatment of complex systems through a wide range of respiratory-related examples. Following a brief historical overview, some of the tools to characterize structural variabilities and temporal fluctuations associated with complex systems are introduced. By invoking the concept of percolation, the notion of multiscale behavior and related modeling issues are discussed. Spatial complexity is then examined in the airway and parenchymal structures with implications for gas exchange followed by a short glimpse of complexity at the cellular and subcellular network levels. Variability and complexity in the time domain are then reviewed in relation to temporal fluctuations in airway function. Next, an attempt is given to link spatial and temporal complexities through examples of airway opening and lung tissue viscoelasticity. Specific examples of possible and more direct clinical implications are also offered through examples of optimal future treatment of fibrosis, exacerbation risk prediction in asthma, and a novel method in mechanical ventilation. Finally, the potential role of the science of complexity in the future of physiology, biology, and medicine is discussed.

The movements made by performers in a skilled quartet: a distinctive pattern, and the function that it serves

When people perform a task as part of a joint action, their behavior is not the same as it would be if they were performing the same task alone, since it has to be adapted to facilitate shared understanding (or sometimes to prevent it). Joint performance of music offers a test bed for ecologically valid investigations of the way non-verbal behavior facilitates joint action. Here we compare the expressive movement of violinists when playing in solo and ensemble conditions. The first violinists of two string quartets (SQs), professional and student, were asked to play the same musical fragments in a solo condition and with the quartet. Synchronized multimodal recordings were created from the performances, using a specially developed software platform. Different patterns of head movement were observed. By quantifying them using an appropriate measure of entropy, we showed that head movements are more predictable in the quartet scenario. Rater evaluations showed that the change does not, as might be assumed, entail markedly reduced expression. They showed some ability to discriminate between solo and ensemble performances, but did not distinguish them in terms of emotional content or expressiveness. The data raise provocative questions about joint action in realistically complex scenarios.

Effects of isoflurane with and without dexmedetomidine or remifentanil on heart rate variability before and after nociceptive stimulation at different multiples of minimum alveolar concentration in dogs

OBJECTIVE

To evaluate the influence of 3 anesthetic protocols and multiples of minimum alveolar concentration (MAC) on heart rate variability (HRV) with and without nociceptive stimulation in dogs.

ANIMALS

6 healthy adult Beagles.

PROCEDURES

Each dog was anesthetized 3 times: with isoflurane alone, with isoflurane and a constant rate infusion of dexmedetomidine (IsoD; 3 μg/kg/h, IV), and with isoflurane and a constant rate infusion of remifentanil (IsoR; 18 μg/kg/h, IV). Individual MAC was determined via supramaximal electrical stimulation. Sinus rhythm-derived intervals between 2 adjacent R-R intervals were exported from ECG recordings. Selected HRV time and frequency domain variables were obtained (at 2-minute intervals) and analyzed offline with signed rank tests before and after stimulation at 0.75, 1.0, and 1. 5 MAC for each anesthetic session.

RESULTS

The isoflurane session had the overall lowest prestimulation SDNN (SD of all R-R intervals) values. Prestimulation SDNN values decreased significantly with increasing MAC in all sessions. For the IsoD session, SDNN (milliseconds) or high-frequency power (milliseconds(2)) was inversely correlated with MAC (Spearman rank correlation coefficient for both variables, -0.77). In the isoflurane and IsoR sessions, heart rate increased significantly after stimulation. In the IsoD session, poststimulation SDNN was increased significantly, compared with prestimulation values, at 0.75 and 1.0 MAC.

CONCLUSIONS AND CLINICAL RELEVANCE

On the basis of SDNN and high-frequency power values, anesthetic levels between 0.75 and 1.5 MAC within the same anesthetic protocol could be differentiated, but with a large overlap among protocols. Usefulness of standard HRV variables for assessment of anesthetic depth and nociception in dogs is questionable.

Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients

A few studies estimating temperature complexity have found decreased Shannon entropy, during severe stress. In this study, we measured both Shannon and Tsallis entropy of temperature signals in a cohort of critically ill patients and compared these measures with the sequential organ failure assessment (SOFA) score, in terms of intensive care unit (ICU) mortality. Skin temperature was recorded in 21 mechanically ventilated patients, who developed sepsis and septic shock during the first 24 h of an ICU-acquired infection. Shannon and Tsallis entropies were calculated in wavelet-based decompositions of the temperature signal. Statistically significant differences of entropy features were tested between survivors and non-survivors and classification models were built, for predicting final outcome. Significantly reduced Tsallis and Shannon entropies were found in non-survivors (seven patients, 33%) as compared to survivors. Wavelet measurements of both entropy metrics were found to predict ICU mortality better than SOFA, according to a combination of area under the curve, sensitivity and specificity values. Both entropies exhibited similar prognostic accuracy. Combination of SOFA and entropy presented improved the outcome of univariate models. We suggest that reduced wavelet Shannon and Tsallis entropies of temperature signals may complement SOFA in mortality prediction, during the first 24 h of an ICU-acquired infection.

In silico modeling: methods and applications to trauma and sepsis

OBJECTIVES

To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis.

DATA SOURCES

PubMed search and review of relevant medical literature.

SUMMARY

Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed.

CONCLUSIONS

Computational modeling of inflammation and organ dysfunction at the cellular, organ, whole-organism, and population levels has suggested a positive feedback cycle of inflammation → damage → inflammation that manifests via organ-specific inflammatory switching networks. This structure may manifest as multicompartment "tipping points" that drive multiple organ dysfunction. This process may be amenable to rational inflammation reprogramming.