Peter Kowallik

Demonstration of nonlinear components in heart rate variability of healthy persons

We present a systematic approach for detecting nonlinear components in heart rate variability (HRV). The analysis is based on twenty-three 48-h Holter recordings in healthy persons during sinus rhythm. Although many segments of 1,024 R-R intervals are stationary, only few stationary segments of 8,192-32,768 R-R intervals can be found using a test of Isliker and Kurths (Int. J. Bifurcation Chaos 3:1573-1579, 1993.). By comparing the correlation integrals from these segments and corresponding surrogate data sets, we reject the null hypothesis that these time series are realization of linear processes. On the basis of a test statistic exploring the differences of consecutive R-R intervals, we reject the hypothesis that the R-R intervals represent a static transformation of a linear process using optimized surrogate data. Furthermore, time irreversibility of the heartbeat data is demonstrated. We interpret these results as a strong evidence for nonlinear components in HRV. Thus R-R intervals from healthy persons contain more information than can be extracted by linear analysis in the time and frequency domain.

Postpericardiotomy syndrome and cardiac tamponade as a late complication after pacemaker implantation.

SPINDLER, M., et al.: Postpericardiotomy Syndrome and Cardiac. Tamponade as a Late Complication After Pacemaker Implantation. In a 78‐year old woman, pacemaker implantation was complicated by a transient perforation of the endocardial lead. The patient was in stable condition for up to 7 weeks after implantation, after which pericardial effusion and subacute cardiac tamponade developed and pericardiocentesis became necessary. This case illustrates that even after initially uneventful pacemaker lead perforation, careful, long‐term follow‐up is necessary to recognize the potential development of late postpericardiotomy syndrome.

DEMONSTRATION OF 1/f FLUCTUATIONS AND WHITE NOISE IN THE HUMAN HEART RATE BY THE VARIANCE-TIME-CURVE: IMPLICATIONS FOR SELF-SIMILARITY

Spectral analysis of heart rate variability is usually performed by Fast Fourier Transform. Here we demonstrate the self-affine properties of the human heart rate using a spectral analysis based on counting statistics. Each QRS complex is considered to be a point event and from the number of events N(Δt) in consecutive time windows Δt the variance is calculated. From the finding that the variance of N(Δt) follows a power law proportional to (Δt)1+b in case of 1/fb noise, it is shown that the variance of the heart rate as determined for windows of length Δt, i.e., N(Δt)/Δt, is proportional to (Δt)b−1. From a 12-day Holter recording, the scaling region could be determined to cover 0.16 to 0.000136 Hz. A function X(t) is self-affine if X(t) and X(rt)/rH have the same distribution functions. From the variance-time-curve, it can be shown that the exponent H is dependent on b with b=2H−1. In young healthy men, the parameter b fluctuates between 0.2 and 1.0 during 24 h and thus determines the self-affine scaling factor H=(b−1)/2 for the amplitude of heart rate, if the time axis is scaled by r. Thus, during periods of 1/f noise, the heart rate scales with H=0, and for periods of almost white noise, with H close to .

Preserved Autonomic Modulation of the Sinus and Atrioventricular Nodes Following Posteroseptal Ablation for Treatment of Atrioventricular Nodal Reentrant Tachycardia

Autonomic Control of the AV Node. Introduction: Following radiofrequency catheter ablation of AV nodal reentrant tachycardia (AVNRT), inappropriate sinus tachycardia may occur, possibly due to damage to autonomic cardiac nerve fibers. Furthermore, inducibility of AVNRT is often critically dependent on the autonomic balance. We investigated whether successful ablation of AVNRT is associated with an alteration of autonomic input to the sinus and AV nodes.

Independent Autonomic Modulation of Sinus Node and Ventricular Myocardium in Healthy Young Men During Sleep

Autonomic Modulation of Sinus Node and Ventricle. Introduction. The aim of this study was to investigate whether autonomic modulation of ventricular repolarization may spontaneousiy differ from that of the sinoatrial node.

Body Surface Potentials During Discharge of the Implantable Cardioverter Defibrillator

Body Surface Potentials During ICD Discharge. Introduction: Little is known about the hazard for persons in contact with patients experiencing a high‐voltage discharge of their implantable cardioverter defibrillator (ICD). Compared to epicardial systems, this risk may be increased with transvenous electrode systems and particularly in active can configurations.

Evolution of Late Potentials During the First 8 Hours of Myocardial Infarction Treated with Thrombolysis

It has been demonstrated that successful thrombolytic therapy is associated with a reduction of late potentials in the signal‐averaged electrocardiogram (SAECG) recorded within 48 hours after hospital admission. This study extends these observations, using for the first time a longitudinal design investigating whether ischemia and its potential reversal by thrombolytic therapy are associated with dynamic changes in SAECG recordings obtained continuously for 8 hours after the start of therapy in patients with acute myocardial infarction (MI). SAECGs were obtained from 12 patients (2 women and 10 men; ages 63 ± 13 years) with acute MI. The SAECG (X2+ Y2+ Z2)1/2 was generated with a high pass filter of 40 Hz, noise ≤ 0.3 μV. Comparing the SAECG recordings during the first and eighth hours, there was a significant decrease in filtered QRS duration (fQRS; 119.5 ± 17.1 vs 106.3 ± 15.3 ms) and duration of the low amplitude signals ≤ 40 μV of the terminal QRS (LAS40; 48.8 ± 18 vs 34.2 ± 14.2 ms), and increase of root mean square voltage of the last 40 ms of the QRS (t‐RMS; 14.8 ± 9.3 vs 37.8 ± 34.4 μV) (rank test, P ≤ 0.05). In this patient series, there was a significant improvement of fQRS, t‐RMS, and LAS40 during the first 8 hours of acute MI, perhaps indicating reversal of ischemia with thrombolysis. Even during acute MI, these markers of delayed conduction allow investigation of intervention induced changes in myocardial conduction and possibly prediction of the patency of the infarct related artery using signal‐averaging techniques.

Heart rate variability and nonlinear dynamics

Zusammenfassung In diesem Review wird die Bedeutung nichtlinearer Methoden bei der Analyse der Herzfrequenzvariabilität erörtert. Zunächst wird die Komplexität des Herzfrequenzsignals veranschaulicht, das eine Fülle von externen und internen Steuermechanismen widerspiegelt. Es wird gezeigt, dass die „Perturbationsanalyse“ ein Weg zur Verbesserung der Analyse der Herzfrequenzvariabiltät darstellt. In diesem systemanalytischen Ansatz wird die unmittelbare Reaktion der Herzfrequenz auf eine spezifische Perturbation und nicht die globale Dynamik einer 24-Stundenaufzeichnung analysiert. Die „heart rate turbulence“ und die „heart rate recovery“ sind prominente Beispiele dieses Ansatzes.¶   Nach einem kurzen Überblick über 1/f-Fluktuationen als dominante Komponente des (linearen) Frequenzspektrums wird der Return Map als ein zentrales Werkzeug nichtlinearer Analysen behandelt. Grundlegende Eigenschaften des Return Maps werden dargestellt, aus denen sich bereits nichtlineare Komponenten des Herzfrequenzsignals ableiten lassen. Praktische Aspekte der Analyse des Return Maps werden ebenfalls aufgezeigt, die sich als sehr fruchtbar bei der Analyse von RR-Intervalldaten erwiesen haben.Summary In this review the importance of nonlinear analysis of heart rate variability is discussed. In the introduction the complexity of the heart rate signal is highlighted which reflects an abundance of external and internal control mechanisms. It is shown that one way to improve analysis of heart rate variability in terms of risk prediction is “perturbation” analysis. In this system analysis approach, the response of heart rate to a specific perturbation rather than the global dynamics of a 24 hour recording are analyzed. Prominent examples of this approach are “heart rate turbulence” and “heart rate recovery”. After mentioning 1/f-fluctuations as the dominant but still linear phenomenon in spectral analysis of heart rate variability, the remainder of the review is focused on the return map. Fundamental properties of the return map of heart rate are delineated which clearly demonstrate nonlinear properties of the heart rate signal. In addition, practical aspects of return map analysis are reviewed which prove to be very fruitful in the handling of heart rate data.

Methods of Non-Linear Dynamics

Noninvasive risk stratification of patients post myocardial infarction has gained renewed interest, since prophylactic therapy for sudden cardiac death has primarily to consider the expensive and invasive therapy with the implantable cardioverter/defibrillator 25. In addition to left ventricular ejection fraction and number of ventricular extrasystoles 3, measures of heart rate variability (HRV) are often used for risk stratification. HRV is usually analyzed in the time and frequency domain 4 17 22. These methods describe the linear correlations in HRV. In addition, nonlinear components in HRV have been demonstrated 20 16 7 and are being explored for improvement of risk prediction 13 29 32. In this article, we briefly review methods that have been used to describe and/or quantify nonlinear components in HRV. Methods using symbolic dynamics are presented elsewhere in this book.