Biomedical subjects

Bernd Pompe

Publications and source records attributed to Bernd Pompe.

6 recordsLinked to original sources

PubMed2006

Autonomic information flow improves prognostic impact of task force HRV monitoring.

Heart rate variability (HRV) represents the cardiovascular control mediated by the autonomic nervous system and other mechanisms. In the established task force HRV monitoring different cardiovascular control mechanisms can approximately be identified at typical frequencies of heart rate oscillations by power spectral analysis. HRV measures assessing complex and fractal behavior partly improved clinical risk stratification. However, their relationship to (patho-)physiology is not sufficiently explored. Objective of the present work is the introduction of complexity measures of different physiologically relevant time scales. This is achieved by a new concept of the autonomic information flow (AIF) analysis which was designed according to task force HRV. First applications show that different time scales of AIF improve the risk stratification of patients with multiple organ dysfunction syndrome and cardiac arrest patients in comparison to standard HRV. Each group's significant time scales correspond to their respective pathomechanisms.

Adult↗

PubMed2006

Analysis of complex physiological systems by information flow: a time scale-specific complexity assessment.

In the last two decades conventional linear methods for biosignal analysis have been substantially extended by non-stationary, non-linear, and complexity approaches. So far, complexity is usually assessed with regard to one single time scale, disregarding complex physiology organised on different time scales. This shortcoming was overcome and medically evaluated by information flow functions developed in our research group in collaboration with several theoretical, experimental, and clinical partners. In the present work, the information flow is introduced and typical information flow characteristics are demonstrated. The prognostic value of autonomic information flow (AIF), which reflects communication in the cardiovascular system, was shown in patients with multiple organ dysfunction syndrome and in patients with heart failure. Gait information flow (GIF), which reflects communication in the motor control system during walking, was introduced to discriminate between controls and elderly patients suffering from low back pain. The applications presented for the theoretically based approach of information flow confirm its value for the identification of complex physiological systems. The medical relevance has to be confirmed by comprehensive clinical studies. These information flow measures substantially extend the established linear and complexity measures in biosignal analysis.

Adult↗

PubMed2005

Prognostic impact of autonomic information flow in multiple organ dysfunction syndrome patients.

BACKGROUND: Multiple organ dysfunction syndrome (MODS) is the sequential failure of several organ systems after a trigger event, like cardiogenic shock or decompensated heart failure. Mortality is high, up to 70%. Autonomic dysfunction (AD) may substantially contribute to the development of MODS. In cardiology, it has recently been shown that nonlinear parameters could predict mortality. Our study aimed at 1. characterising the complex characteristics of AD of critically ill MODS patients by the nonlinear parameters of autonomic information flow (AIF), 2. comparing AIF with autonomic function of healthy controls, and 3. characterising the accuracy of this parameter in predicting mortality in MODS. METHODS: We enrolled 43 score-defined MODS patients who were consecutively admitted to a twelve-bed medical intensive care unit in a university centre into this prospective outcome study. Additionally, we assigned 50 healthy controls to the study. AIF was assessed as a complexity function of AD using 24-h ECG. Measures of AIF were introduced according to the standard HRV concept. The patients were followed up for 28-day mortality. RESULTS: MODS causes a disorganisation of short term AIF in favour of an enhanced (rigid) long term AIF. Concerning prognosis increased short term AIF was associated with survival. Short term AIF discriminated between MODS survivors and non-survivors at the level of APACHE II score. CONCLUSIONS: This is the first study providing evidence that complex AD of MODS patients is specifically assessed by AIF time scales and that AIF has significant prognostic impact.

Adult↗

PubMed2005

Mutual information function assesses autonomic information flow of heart rate dynamics at different time scales.

The autonomic information flow (AIF) represents the complex communication within the Autonomic Nervous System (ANS). It can be assessed by the mutual information function (MIF) of heart rate fluctuations (HRF). The complexity of HRF is based on several interacting physiological mechanisms operating at different time scales. Therefore one prominent time scale for HRF complexity analysis is not given a priori. The MIF reflects the information flow at different time scales. This approach is defined and evaluated in the present paper. In order to aggregate relevant physiological time scales, the MIF of HRF obtained from eight adult Lewis rats during the awake state, under general anesthesia, with additional vagotomy, and additional betal-adrenergic blockade are investigated. Physiologically relevant measures of the MIF were assessed with regard to the discrimination of these states. A simulation study of a periodically excited pendulum is performed to clarify the influence of the time scale of MIF in comparison to the Kolmogorov Sinai entropy (KSE) of that well defined system. The general relevance of the presented AIF approach was confirmed by comparing mutual information, approximate entropy, and sample entropy at their respective time scales.

Algorithms↗

PubMed2002

Permutation entropy: a natural complexity measure for time series.

We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical systems it is shown that our complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise. The advantages of our method are its simplicity, extremely fast calculation, robustness, and invariance with respect to nonlinear monotonous transformations.

Journal Article↗

PubMed2002

Mutual information and phase dependencies: measures of reduced nonlinear cardiorespiratory interactions after myocardial infarction.

The heart rate variability (HRV) is related to several mechanisms of the complex autonomic functioning such as respiratory heart rate modulation and phase dependencies between heart beat cycles and breathing cycles. The underlying processes are basically nonlinear. In order to understand and quantitatively assess those physiological interactions an adequate coupling analysis is necessary. We hypothesized that nonlinear measures of HRV and cardiorespiratory interdependencies are superior to the standard HRV measures in classifying patients after acute myocardial infarction. We introduced mutual information measures which provide access to nonlinear interdependencies as counterpart to the classically linear correlation analysis. The nonlinear statistical autodependencies of HRV were quantified by auto mutual information, the respiratory heart rate modulation by cardiorespiratory cross mutual information, respectively. The phase interdependencies between heart beat cycles and breathing cycles were assessed basing on the histograms of the frequency ratios of the instantaneous heart beat and respiratory cycles. Furthermore, the relative duration of phase synchronized intervals was acquired. We investigated 39 patients after acute myocardial infarction versus 24 controls. The discrimination of these groups was improved by cardiorespiratory cross mutual information measures and phase interdependencies measures in comparison to the linear standard HRV measures. This result was statistically confirmed by means of logistic regression models of particular variable subsets and their receiver operating characteristics.

Aged↗