A wavelet-based heart rate variability analysis for the study of nonsustained ventricular tachycardia

It has been reported that the sympathovagal balance (SB) can be quantified by heart rate (HR) via the low-frequency (LF) to high-frequency (HF) spectral power ratio LF/HF. In this paper, an investigation of the relationship between the autonomic nervous system (ANS) and non-sustained ventricular tachycardia (NSVT) is presented. A wavelet transform (WT)-based approach for short-time heart rate variability (HRV) assessments is proposed for this aspect of analysis. The study was conducted on an RR-interval database consisting of 87 NSVT, 61 ischemic and five normal episodes. First, instantaneous SB estimates were generated by the proposed method. Then, waveforms of the WT-based SB evolutions were quantitatively examined. Numerical results showed that while a majority of SB waveforms (about 71%) derived from the non-NSVT population (i.e., ischemic and normal) appeared to come near oscillating with certain fixed levels, approximate 75% of SB evolutions underwent significantly rapid increases prior to the onset of NSVT, suggesting that an abrupt sympathovagal imbalance might partly account for the occurrence of NSVT.

[1]  S. Huang,et al.  Nonsustained ventricular tachycardia: identification and management of high-risk patients. , 1993, American heart journal.

[2]  Jacek M. Zurada,et al.  Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images , 1996, IEEE Trans. Medical Imaging.

[3]  N.V. Thakor,et al.  Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection , 1991, IEEE Transactions on Biomedical Engineering.

[4]  W. J. Tompkins,et al.  Detecting ventricular fibrillation , 1995 .

[5]  L. Tsimring,et al.  The analysis of observed chaotic data in physical systems , 1993 .

[6]  R J Cohen,et al.  Beat to beat variability in cardiovascular variables: noise or music? , 1989, Journal of the American College of Cardiology.

[7]  K. Narayanan,et al.  On the evidence of deterministic chaos in ECG: Surrogate and predictability analysis. , 1998, Chaos.

[8]  A. Gualtierotti H. L. Van Trees, Detection, Estimation, and Modulation Theory, , 1976 .

[9]  A. Casaleggio,et al.  Estimation of Lyapunov exponents of ECG time series—The influence of parameters , 1997 .

[10]  R. Klein,et al.  Use of electrophysiologic testing in patients with nonsustained ventricular tachycardia: prognostic and therapeutic implications. , 1989, Journal of the American College of Cardiology.

[11]  G. Baselli,et al.  Pole-tracking algorithms for the extraction of time-variant heart rate variability spectral parameters , 1995, IEEE Transactions on Biomedical Engineering.

[12]  M T Arredondo,et al.  Ventricular fibrillation detection by autocorrelation function peak analysis. , 1989, Journal of electrocardiology.

[13]  N. Thakor,et al.  Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm , 1990, IEEE Transactions on Biomedical Engineering.

[14]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[15]  L E Hinkle,et al.  Clinical Classification of Cardiac Deaths , 1982, Circulation.

[16]  P Coumel,et al.  Heart rate variability and the onset of tachyarrhythmias. , 1992, Giornale italiano di cardiologia.

[17]  V Pichot,et al.  Wavelet transform to quantify heart rate variability and to assess its instantaneous changes. , 1999, Journal of applied physiology.

[18]  Tapio Seppänen,et al.  Frequency Domain Measures of Heart Rate Variability Before the Onset of Nonsustained and Sustained Ventricular Tachycardia in Patients With Coronary Artery Disease , 1993, Circulation.

[19]  H. V. Trees Detection, Estimation, And Modulation Theory , 2001 .

[20]  N.V. Thakor,et al.  Multiway sequential hypothesis testing for tachyarrhythmia discrimination , 1994, IEEE Transactions on Biomedical Engineering.