Nonlinear short-term heart rate variability prediction of spontaneous ventricular tachyarrhythmia

As malign ventricular tachyarrhythmias triggering sudden cardiac death (SCD), both ventricular tachycardia (VT) and ventricular fibrillation (VF) are major causes of mortality. The most efficient therapy for SCD prevention is implantable cardioverter defibrillators (ICD). The ICD can accurately and effectively identify the forthcoming of fatal ventricular tachyarrhythmias and deliver a shock in order to restore patients’ normal sinus rhythm. In this study, two nonlinear complexity measures based on entropy: approximate entropy (ApEn) and sample entropy (SampEn) as well as two time linear indices: the mean RR interval (the average of time intervals between consecutive R-waves) and the standard deviation of RR intervals were used for short-term forecasting of VT-VF occurrence. The last small sections of interbeat intervals preceding 135 VT-VF episodes from 78 patients stored by the ICD were analyzed and compared with individually acquired control time series (CON series) from the same patients, which are normally intrinsic sinus rhythms. The results demonstrate that in addition to an obvious increase in heart rates of the patients, the values of two entropy measures are significantly smaller for VT-VF episodes than those for CON series. Conclusions can be drawn that when a ventricular tachyarrhythmia approaches, the sympathetic tone of the patients is increased, and the complexity of their RR intervals immediately before the onset of VT-VF events is obviously lower than that of RR intervals recorded during sinus rhythms. For a better separation, the optimal range of threshold r is determined for two algorithms. ApEn and SampEn measures might be the suitable nonlinear parameters for short-term prediction of life-threatening ventricular tachyarrhythmias in the application of the cardioversion and defibrillation. Supported by the National Natural Science Foundation of China (Grant Nos. 60501003, 60701002)