Hagen Malberg

Nonlinear analysis of complex phenomena in cardiological data

Zusammenfassung Das Hauptanliegen dieses Beitrages ist es, verschiedene Ansätze in der Herzfrequenz- und Blutdruckvariabilität zu diskutieren und damit das Verständnis der kardiovaskulären Regulation zu verbessern. Wir betrachten Komplexitätsmaße basierend auf der symbolischen Dynamik, die renormierte Entropie und die ,finite-time’ Wachstumsraten. Weiterhin werden die duale Sequenzmethode zur Bestimmung der Baroreflexsensitivität sowie die Maximalkorrelationsmethode zur Schätzung der nichtlinearen Kopplung in bivariaten Daten vorgestellt. Letztere stellt eine geeignete Methode zur Bestimmung der Kopplungsstärke und –richtung dar. Herzfrequenz- und Blutdruckvariabilitätsdaten einer klinischen Pilotstudie und einer großangelegten klinischen Studie werden analysiert. Wir demonstrieren in diesem Beitrag, dass Methoden der nichtlinearen Dynamik nützlich sind für die Risikostratifizierung nach Herzinfarkt, für die Vorhersage von lebensbedrohlichen Rhythmusstörungen sowie für die Modellierung der Herzfrequenz- und Blutdruckregulation. Diese Ergebnisse könnten in der klinischen Diagnostik sowie für therapeutische und präventive Zwecke von implantierbaren Defibrillatoren der nächsten Generation von Bedeutung sein.Summary The main intention of this contribution is to discuss different nonlinear approaches to heart rate and blood pressure variability analysis for a better understanding of the cardiovascular regulation. We investigate measures of complexity which are based on symbolic dynamics, renormalised entropy and the finite time growth rates. The dual sequence method to estimate the baroreflex sensitivity and the maximal correlation method to estimate the nonlinear coupling between time series are employed for analysing bivariate data. The latter appears to be a suitable method to estimate the strength of the nonlinear coupling and the coupling direction. Heart rate and blood pressure data from clinical pilot studies and from very large clinical studies are analysed. We demonstrate that parameters from nonlinear dynamics are useful for risk stratification after myocardial infarction, for the prediction of life-threatening cardiac events even in short time series, and for modelling the relationship between heart rate and blood pressure regulation. These findings could be of importance for clinical diagnostics, in algorithms for risk stratification, and for therapeutic and preventive tools of next generation implantable cardioverter defibrillators.

Baroreflex sensitivity, heart rate, and blood pressure variability in normal pregnancy

Heart rate variability is a relevant predictor of cardiovascular risk in humans. However, to use heart and blood pressure (BP) variability or baroreflex sensitivity as markers for hypertensive pregnancy disorders, it is first necessary to describe these parameters in normal pregnancy. To accommodate the complexities of autonomic cardiovascular control we added parameter domains of nonlinear dynamics to conventional linear methods of time and frequency domains. The BP of 27 women with normal pregnancy and 14 nonpregnant women were monitored at a high resolution (200 Hz sampling frequency) using a Portapres for 30 min. The pregnant women were divided into groups of 32 or less or greater than 32 weeks of gestation. Pregnant and nonpregnant women were classified into subclasses of maternal age of less than 28 or 28 or more years. Except for two single parameter domains, we found no significant differences in heart rate and BP variability for pregnant women with different gestational age or different maternal age. Moreover, no significant differences in spontaneous baroreflex sensitivity could be found between pregnant women regardless of either their age or gestational age. In contrast, all measures of nonlinear dynamics of heart rate variability as well as all parameter domains of spontaneous baroreflex sensitivity showed significant changes between pregnant and nonpregnant women, whereas BP variability did not differ between those groups. This complex assessment of autonomic cardiovascular regulation has shown that the parameters tested are stable in the second half of normal pregnancy, and might have the potential to be excellent indicators of pathophysiologic conditions.

NONLINEAR METHODS OF CARDIOVASCULAR PHYSICS AND THEIR CLINICAL APPLICABILITY

In this tutorial we present recently developed nonlinear methods of cardiovascular physics and show their potentials to clinically relevant problems in cardiology. The first part describes methods of cardiovascular physics, especially data analysis and modeling of noninvasively measured biosignals, with the aim to improve clinical diagnostics and to improve the understanding of cardiovascular regulation. Applications of nonlinear data analysis and modeling tools are various and outlined in the second part of this tutorial: monitoring-, diagnosis-, course and mortality prognoses as well as early detection of heart diseases. We show, that these data analyses and modeling methods lead to significant improvements in different medical fields.

Altered heart rate and blood pressure variability in mice lacking the Mas protooncogene

Heart rate variability is a relevant predictor of cardiovascular risk in humans. A significant genetic influence on heart rate variability is suggested, although the genes involved are ill-defined. The Mas-protooncogene encodes a G-protein-coupled receptor with seven transmembrane domains highly expressed in testis and brain. Since this receptor is supposed to interact with the signaling of angiotensin II, which is an important regulator of cardiovascular homeostasis, heart rate and blood pressure were analyzed in Mas-deficient mice. Using a femoral catheter the blood pressure of mice was measured for a period of 30 min and 250 data values per second were recorded. The mean values and range of heart rate and blood pressure were then calculated. Neither heart rate nor blood pressure were significantly different between knockout mice and controls. However, high resolution recording of these parameters and analysis of the data by non-linear dynamics revealed significant alterations in cardiovascular variability in Mas-deficient animals. In particular, females showed a strong reduction of heart rate variability. Furthermore, the data showed an increased sympathetic tone in knockout animals of both genders. The marked alterations detected in Mas-deficient mice of both genders suggest that the Mas-protooncogene is an important determinant of heart rate and blood pressure variability.

Detection of time-delayed interactions in biosignals using symbolic coupling traces

Directional coupling analysis of bivariate time series is an important subject of current research. In this letter, a method based on symbolic dynamics for the detection of time-delayed coupling is presented. The symbolic coupling traces, defined as the symmetric and diametric traces of the bivariate word distribution, allow for the quantification of coupling and are compared with established methods like mutual information and cross recurrence analysis. The symbolic coupling traces method is applied to model systems and cardiological data which demonstrate its advantages especially for nonstationary data.

Cardiovascular and respiratory dynamics during normal and pathological sleep.

Sleep is an active and regulated process with restorative functions for physical and mental conditions. Based on recordings of brain waves and the analysis of characteristic patterns and waveforms it is possible to distinguish wakefulness and five sleep stages. Sleep and the sleep stages modulate autonomous nervous system functions such as body temperature, respiration, blood pressure, and heart rate. These functions consist of a sympathetic tone usually related to activation and to parasympathetic (or vagal) tone usually related to inhibition. Methods of statistical physics are used to analyze heart rate and respiration to detect changes of the autonomous nervous system during sleep. Detrended fluctuation analysis and synchronization analysis and their applications to heart rate and respiration during sleep in healthy subjects and patients with sleep disorders are presented. The observed changes can be used to distinguish sleep stages in healthy subjects as well as to differentiate normal and disturbed sleep on the basis of heart rate and respiration recordings without direct recording of brain waves. Of special interest are the cardiovascular consequences of disturbed sleep because they present a risk factor for cardiovascular disorders such as arterial hypertension, cardiac ischemia, sudden cardiac death, and stroke. New derived variables can help to find indicators for these health risks.

Analysis of cardiovascular oscillations: A new approach to the early prediction of pre-eclampsia

Pre-eclampsia (PE) is a serious disorder with high morbidity and mortality occurring during pregnancy; 3%-5% of all pregnant women are affected. Early prediction is still insufficient in clinical practice. Although most pre-eclamptic patients show pathological uterine perfusion in the second trimester, this parameter has a positive predictive accuracy of only 30%, which makes it unsuitable for early, reliable prediction. The study is based on the hypothesis that alterations in cardiovascular regulatory behavior can be used to predict PE. Ninety-six pregnant women in whom Doppler investigation detected perfusion disorders of the uterine arteries were included in the study. Twenty-four of these pregnant women developed PE after the 30th week of gestation. During pregnancy, additional several noninvasive continuous blood pressure recordings were made over 30 min under resting conditions by means of a finger cuff. The time series extracted of systolic as well as diastolic beat-to-beat pressures and the heart rate were studied by variability and coupling analysis to find predictive factors preceding genesis of the disease. In the period between the 18th and 26th weeks of pregnancy, three special variability and baroreflex parameters were able to predict PE several weeks before clinical manifestation. Discriminant function analysis of these parameters was able to predict PE with a sensitivity and specificity of 87.5% and a positive predictive value of 70%. The combined clinical assessment of uterine perfusion and cardiovascular variability demonstrates the best current prediction several weeks before clinical manifestation of PE.

A combined technique for predicting pre-eclampsia: concurrent measurement of uterine perfusion and analysis of heart rate and blood pressure variability.

Objective Pre-eclampsia is a serious complication of pregnancy with high morbidity and mortality and an incidence of 3–5% in all pregnancies. Early prediction is still insufficient in clinical practice. Although most pre-eclamptic patients have pathological uterine perfusion in the second trimester, perfusion disturbance has a positive predictive accuracy (PPA) only of approximately 30%. Methods Non-invasive continuous blood pressure recordings were taken simultaneously via a finger cuff for 30 min. Time series of systolic as well as diastolic beat-to-beat pressure values were extracted to analyse heart rate and blood pressure variability and baroreflex sensitivity in 102 second-trimester pregnancies, to assess predictability for pre-eclampsia (n = 16). All women underwent Doppler investigations of the uterine arteries. Results We identified a combination of three variability and baroreflex parameters to best predict pre-eclampsia several weeks before clinical manifestation. The discriminant function of these three parameters classified patients with later pre-eclampsia with a sensitivity of 87.5%, a specificity of 83.7%, and a PPA of 50.0%. Combined with Doppler investigations of uterine arteries, PPA increased to 71.4%. Conclusions This technique of incorporating one-stop clinical assessment of uterine perfusion and variability parameters in the second trimester produces the most effective prediction of pre-eclampsia to date.

Comparison of three methods for beat-to-beat-interval extraction from continuous blood pressure and electrocardiogram with respect to heart rate variability analysis / Vergleich von drei Methoden der Schlag-zu-Schlag-Intervall-Extraktion aus kontinuierlichen Blutdruckverläufen und Elektrokardiogrammen zur Herzratenvariabilitätsanalyse

Abstract In recent years the analysis of heart rate variability (HRV) has become a suitable method for characterizing autonomous cardiovascular regulation. The aim of this study was to investigate the differences in HRV estimated from continuous blood pressure (BP) measurement by different methods in comparison to electrocardiogram (ECG) signals. The beat-to-beat intervals (BBI) were simultaneously extracted from the ECG and blood pressure of 9 cardiac patients (10 min, Colin system, 1000-Hz sampling frequency). For both data types, slope, peak, and correlation detection algorithms were applied. The short-term variability was calculated using concurrent 10-min BP and ECG segments. The root mean square errors in comparison to ECG slope detection were: 1.74 ms for ECG correlation detection; 5.42 ms for ECG peak detection; 5.45 ms for BP slope detection; 5.75 ms for BP correlation detection; and 11.96 ms for BP peak detection. Our results show that the variability obtained with ECG is the most reliable. Moreover, slope detection is superior to peak detection and slightly superior to correlation detection. In particular, for ECG signals with higher frequency characteristics, peak detection often exhibits more artificial variability. Besides measurement noise, respiratory modulation and pulse transit time play an important role in determining BBI. The slope detection method applied to ECG should be preferred, because it is more robust as regards morphological changes in the signals, as well as physiological properties. As the ECG is not recorded in most animal studies, distal pulse wave measurement in combination with correlation or slope detection may be considered an acceptable alternative.

Autonomic Control in Patients Experiencing Atrial Fibrillation After Cardiac Surgery

Background: Atrial fibrillation (AF) occurs in 20–40% of patients after open heart surgery and leads to an increased morbidity and prolonged hospital stay. Earlier studies have demonstrated that depressed baroreflex function predicts mortality and major arrhythmic events in patients surviving myocardial infarction. Cardiac surgery per se leads to decreased baroreflex sensitivity (BRS) and heart rate variability (HRV). Hence, the present study was aimed at analyzing the impact of the cardiovascular autonomous system on the development of postsurgical AF.