Abstract
A dynamic linear parametric model is designed to quantify the dependence of ventricular repolarisation duration variability on heart period changes and other immeasurable factors. The model analyses the beat-to-beat series of the RR duration and of the interval between R- and T-wave apexes (RT period). Directly from these two signals, a parametric identification procedure and spectral decomposition techniques allow RT variability to be divided into RR-related and RR-unrelated parts and allow the RT-RR transfer function to be calculated. RT variability is driven by RR changes at low frequency (LF, around 0.1 Hz) and high frequency (HF, at the respiratory rate), whereas, at very low frequencies, the RR-unrelated contribution to the total RT variability is remarkable. During tilt at LF the RR-related RT percentage power increases (p<0.02), the RR-unrelated RT percentage power remains unchanged, the gain of the RT-RR relationship largely increases (p<0.001), and the phase is not significantly modified. Both the RR-related and the RR-unrelated RT percentage powers at LF are not affected by controlled respiration, and an increase in the RT-RR gain at HF is observed (p<0.02). The proposed analysis may help to describe the regulation of the ventricular repolarisation process and to extract indexes quantifying the coupling between heart period and ventricular repolarisation interval changes.
Similar content being viewed by others
References
Akaike, H. (1974): ‘A new look at the statistical model identification’,IEEE Trans. Autom. Contr,19, pp. 716–723
Baselli, G., Porta, A., Ferrari, G., Cerutti, S., Rimoldi, O., Pagani, M., andMalliani, A. (1993): ‘Multivariate ARMA spectral decomposition in the assessment of cardiovascular variabilities’. Comp. in Cardiol. Conf., (IEEE Computer Society Press), pp. 731–734
Baselli, G., Porta, A., Rimoldi, O., Pagani, M., andCerutti S. (1997): ‘Spectral decomposition in multi-channel recordings based on multi-variate parametric identification’,IEEE Trans.,BME-44, pp. 1092–1101
Bazett, H. C. (1920): ‘An analysis of the time-relations of electrocardiograms’,Heart,7, pp. 353–370
Bexton, R. S., Vallin, H. O., andCamm, A. J. (1986): ‘Diurnal variation of the QT interval: influence of the autonomic nervous system’,Br. Heart J.,55, pp. 253–258
Browne, K. F., Prystowsky, E., Heger, J. J., andZipes, D. P. (1983): ‘Modulation of the Q-T interval by the autonomic nervous system’,Pace,6, pp. 1050–1055
Franz, M. R., Swerdlow, C. D., Liem, B. L., andSchaefer, J. (1989): ‘Cycle-length dependence of human ventricular action potential duration in steady and non-steady state’,in Butrous, G. S., andSchwartz, P. J. (Eds.): ‘Clinical aspects of ventricular repolarization’ (Farend Press, London) pp. 163–174
Johnsen, S. J., andAndersen, N. (1978): ‘On power estimation in maximum entropy spectral analysis’,Geophysics,43, pp. 681–690
Kay, S. M. (1988): ‘Modern spectral analysis: theory and applications’ (Prentice Hall, Englewood Cliffs, New Jersey)
Lau, C. P. andWard, J. (1989): ‘QT hysteresis: the effects of an abrupt change in pacing rate’ inButrous, G. S. andSchwartz, P. J. (Eds.): ‘Clinical aspects of ventricular repolarization’ (Farrand Press, London) pp. 175–184
Ljung, L., (1987): ‘System identification. Theory and methods’ (Prentice Hall, Englewood Cliffs, New Jersey)
Lombardi, F., Sandrone, G., Porta, A., Torzillo, D., Terranova, G., Baselli, G., Cerutti, S., andMalliani, A. (1996): ‘Spectral analysis of short term R-Tapex interval variability during sinus rhythm and fixed atrial rate’,Eur. Heart J.,17, pp. 769–778
Maison Blanche, P., Catuli, D., Fayn, J., andCoumel, P., (1996): ‘QT interval, heart rate and ventricular arrhythmias’,in Moss, A., andStern, S., (Eds.): ‘Non-invasive electrocardiology. Aspects of Holter monitoring’ (W. B. Saunders Company Ltd, London) pp. 383–404
Merri, M., Alberti, M., andMoss, A. J. (1993): ‘Dynamic analysis of ventricular repolarisation duration from 24-hour Holter recordings’,IEEE Trans.,BME-40, pp. 1219–1225
Nollo, G., Speranza, G., Grasso, R., Bonamini, R., Mangiardi, L., andAntolini, R. (1992): ‘Spontaneous beat-to-beat variability of the ventricular repolarisation duration’,J. Electrocardiol.,25, pp. 9–17
Porta, A., Lombardi, F., Benedetti, M., Sandrone, G., Baselli, G., Malliani, A., andCerutti, S., (1994): ‘Reliability of the measurement of the RT variability’. Comp. in Cardiol. Conf. (IEEE Computer Society Press) pp. 217–220
Porta, A., Baselli, G. E., Caiani, G. Scarpellini, Sandrone, G., Malliani, A., Cerutti, S., andLombardi, F., (1996): ‘Model for RT-RR variability interaction assessment’. Comp. in Cardiol. Conf., (IEEE Computer Society Press) pp. 281–284
Schwartz, P. J., Periti, M., andMalliani, A., (1975) ‘The long Q-T syndrome’,Am. Heart J.,89, pp. 378–390
Söderström, T. (1974): ‘On the convergence properties of the generalised least squares identification method’,Automatica,10, pp. 617–626
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Porta, A., Baselli, G., Caiani, E. et al. Quantifying electrocardiogram RT-RR variability interactions. Med. Biol. Eng. Comput. 36, 27–34 (1998). https://doi.org/10.1007/BF02522854
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02522854