Recurrence Plot Based Measures of Complexity and its Application to Heart Rate Variability Data

The knowledge of transitions between regular, laminar or chaotic behavior is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods which however require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart rate variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e. chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our new measures to the heart rate variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias

Forecasting of life threatening arrhythmias using the compression entropy of heart rate

Objectives: Ventricular tachycardia (VT) provoking sudden cardiac death (SCD) are a major cause of mortality in the developed countries. The most efficient therapy for SCD prevention are implantable cardioverter defibrillators (ICD). In this study heart rate variability (HRV) measures were analyzed for short-term forecasting of VT in order to improve VT sensing and to enable a patient warning of forth-coming shocks. Methods: The last 1000 normal beat-to-beat intervals before 50 VT episodes stored by the ICD were analyzed and compared to individually acquired control time series (CON). HRV analysis was performed with standard parameters of time and frequency domain as suggested by the HRV Task Force and furthermore with a newly developed and optimized nonlinear parameter that assesses the compression entropy of heart rate (Hc ). Results: Except of meanNN (p = 0.02) we found no significant differences in standard HRV parameters. In contrast, Hc revealed highly significant (p = 0.007) alterations in VT compared with CON suggesting a decreased complexity before the onset of VT. Conclusion: Compression entropy might be a suitable parameter for short-term forecasting of life-threatening tachycardia in ICD.M. Baumert, V. Baier, J. Haueisen, N. Wessel, U. Meyerfeldt, A. Schirdewan, and A. Vos

Heart rate variability before the onset of ventricular tachycardia: differences between slow and fast arrhythmias

Background: We tested whether or not heart rate variability (HRV) changes can serve as early signs of ventricular tachycardia (VT) and predict slow and fast VT in patients with an implantable cardioverter defibrillator (ICD). Methods and results: We studied the ICD stored 1000 beat-to-beat intervals before the onset of VT (131 episodes) and during a control time without VT (74 series) in 63 chronic heart failure ICD patients. Standard HRV parameters as well as two nonlinear parameters, namely ‘Polvar10’ from symbolic dynamics and the finite time growth rates ‘Fitgra9’ were calculated. Comparing the control and the VT series, no linear HRV parameter showed a significant difference. The nonlinear parameters detected a significant increase in short phases with low variability before the onset of VT (for time series with less than 10% ectopy, P270 ms) events, we found that the onset of slow VT was characterized by a significant increase in heart rate, whereas fast VT was triggered during decreased heart rates, compared to the control series. Conclusions: Our data may permit the development of automatic ICD algorithms based on nonlinear dynamic HRV parameters to predict VT before it starts. Furthermore, they may facilitate improved prevention strategies