Abstract
Uncovering of the atrial signal for patients undergoing episodes of atrial fibrillation is usually obtained from surface ECG by removing waves induced by ventricular activities. Once earned the atrial signal, the detection of the dominant fibrillation frequency is often the main (and only) goal. In this work we verified if subtraction of the ventricular activity might be avoided by performing spectral analysis on those ECG segments where ventricular activity is absent, (i.e. the T-Q intervals). While the approach might seem crude, in here the question was recast into a problem of missing data in a long time series and proper methods were applied: the Lomb periodogram and the iterative Singular Spectrum Analysis. The two methods were tested on both simulated signals and “realistic” atrial signals constructed using the ECG recordings provided by the 2004 Computers in Cardiology competition. The results obtained showed that both techniques were able to provide a reliable quantification of the dominant oscillation, with a slightly superior performance of the iterative Singular Spectrum Analysis. Absolute errors larger than 1.0 Hz were unlikely (p < 0.05) up to 130−140 bpm. Such level of agreement is consistent with similar comparative works where techniques for separating the atrial signal from ventricular waves were considered.
Similar content being viewed by others
References
Bollmann, A., D. Husser, L. Mainardi, F. Lombardi, P. Langley, A. Murray, J. Rieta, J. Millet, B. Olsson, M. Stridh, and L. Sörnmo. Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications. Europace 8:911–926, 2006.
Bollmann, A., N. K. Kanuru, K. K. McTeague, P. F. Walter, D. B. DeLurgio, and J. J. Langberg. Frequency analysis of human atrial fibrillation using the surface electrocardiogram and its response to ibutilide. Am. J. Cardiol. 81(12):1439–1445, 1998.
Bollmann, A., K. Sonne, H. D. Esperer, I. Toepffer, J. J. Langberg, and H. U Klein. Non-invasive assessment of fibrillatory activity in patients with paroxysmal and persistent atrial fibrillation using the holter ecg. Cardiovasc. Res. 44(1):60–66, 1999.
Bollmann, A., K. Wodarz, H. D. Esperer, I. Toepffer, and H. U. Klein. Response of atrial fibrillatory activity to carotid sinus massage in patients with atrial fibrillation. Pacing Clin. Electrophysiol. 24(9 Pt 1):1363–1368, 2001.
Broomhead, D. S., and G. P. King. Extracting qualitative dynamics from experimental data. Physica D 20:217–236, 1986.
Castells, F., P. Laguna, L. Sornmo, A. Bollmann, and J. Millet. Principal component analysis in ECG signal processing. EURASIP J. Adv. Signal Process. 2007:74580-21, 2007.
Castells, F., R. Ruiz, J. J. Rieta, and J. Millet. An integral atrial wave identification based on spatiotemporal source separation: clinical validation. IEEE Comput. Cardiol. 30:717–720, 2003.
Castiglioni, P., and M. Di Rienzo. On the evaluation of heart rate spectra: the lomb periodogram. IEEE Comput. Cardiol. 23:505–508, 1996.
Cerutti, S., G. Baselli, S. Civardi, E. Ferrazzi, G. Pardi, A. M. Marconi, and M. Pagani. Circadian modulation of atrial cycle length in human chronic permanent atrial fibrillation: a non-invasive assessment using long-term surface ecg. J. Perinat. Med. 14:445–452, 1986.
Clifford, G. D., and L. Tarassenko. Quantifying errors in spectral estimates of HRV due to beat replacement and resampling. IEEE Trans. Biomed. Eng. 52:630–638, 2005.
Cosson, S., P. Maison-Blanche, S. Olsson, A. Leenhardt, F. Badilini, and P. Coumel. Circadian modulation of atrial cycle length in human chronic permanent atrial fibrillation: a non-invasive assessment using long-term surface ecg. Ann. Noninvasive Electrocardiol. 5:270–278, 2000.
Fujiki, A., H. Nagasawa, M. Sakabe, K. Sakurai, K. Nishida, K. Mizumaki, and H. Inoue. Spectral characteristics of human atrial fibrillation waves of the right atrial free wall with respect to the duration of atrial fibrillation and effect of class i antiarrhythmic drugs. Jpn. Circ. J. 65(12):1047–1051, 2001.
Fujiki, A., M. Sakabe, K. Nishida, K. Mizumaki, and H. Inoue. Role of fibrillation cycle length in spontaneous and drug-induced termination of human atrial fibrillation. Circ. J. 67(5):391–395, 2003.
Holm, M., S. Pehrson, M. Ingemansson, L. Sornmo, R. Johansson, L. Sandhall, M. Sunemark, B. Smideberg, C. Olsson, and S. B. Olsson. Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: introducing, validating and illustrating a new ECG method. Cardiovasc. Res. 38:69–81, 1998.
Ingemansson, M. P., M. Holm, and S.B. Olsson. Autonomic modulation of the atrial cycle length by the head up tilt test: non-invasive evaluation in patients with chronic atrial fibrillation. Heart 80(1):71–76, 1998.
Kondrashov, D., Y. Feliks, and M. Ghil. Oscillatory modes of extended Nile River records (A.D. 622–1922). Geophys. Res. Lett. 32:L10702, 2005.
Kondrashov, D., and M. Ghil. Spatio-temporal filling of missing points in geophysical data sets. Nonlinear Process. Geophys. 13:151–159, 2006.
Laguna, P., G. B. Moody, and R. G. Mark. Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE Trans. Biomed. Eng. 45:698–715, 1998.
Langley, P., J. J. Rieta, M. Stridh, J. Millet-Roig, L. Sornmo, and A. Murray. Reconstruction of atrial signals derived from the 12-lead ECG using atrial signal extraction techniques. IEEE Comput. Cardiol. 30:129–132, 2003.
Langley, P., J. J. Rieta, M. Stridh, J. Millet, L. Sornmo, and A. Murray. Comparison of atrial signal extraction algorithms in 12-lead ECGs with atrial fibrillation. IEEE Trans. Biomed. Eng. 53:343–346, 2006.
Lemay, M., V. Jacquemet, A. Forclaz, J. M. Vesin, and L. Kappenberger. Spatiotemporal QRST cancellation method using separate QRS and T-waves templates. IEEE Comput. Cardiol. 32:611–614, 2005.
Lian, J., D. Müssig, and V. Lang. Computer modeling of ventricular rhythm during atrial fibrillation and ventricular pacing. IEEE Trans. Biomed. Eng. 53:1512–1520, 2006.
Lomb, N. R. Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39:447–462, 1976.
Mainardi, L. T., M. Matteucci, and R. Sassi. On predicting the spontaneous termination of atrial fibrillation episodes using linear and nonlinear parameters of ECG signal and RR series. IEEE Comput. Cardiol. 31:665–668, 2004.
Mainardi, L., L. Sornmo, and S. Cerutti (eds.). Understanding Atrial Fibrillation: The Signal Processing Contribution. California: Morgan & Claypool Publisher, 2008.
Meurling, C. J., J. E. Waktare, F. Holmqvist, A. Hedman, A. J. Camm, S. B. Olsson, and M. Malik. Diurnal variations of the dominant cycle length of chronic atrial fibrillation. Am. J. Physiol. Heart Circ. Physiol. 280(1):H401–H406, 2001.
Moody, G. B. Spectral analysis of heart rate without resampling. IEEE Comput. Cardiol. 20:715–718, 1993.
Moody, G. B. Spontaneous termination of atrial fibrillation: a challenge from physionet and computers in cardiology 2004. IEEE Comput. Cardiol. 31:101–104, 2004.
Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, 1992.
Raine, D., P. Langley, A. Murray, A. Dunuwille, and J. P. Bourke. Surface atrial frequency analysis in patients with atrial fibrillation: a tool for evaluating the effects of intervention. J. Cardiovasc. Electrophysiol. 15:1021–1026, 2004.
Rieta, J. J., F. Castells, C. Sanchez, V. Zarzoso, and J. Millet. Atrial activity extraction for atrial fibrillation analysis using blind source separation. IEEE Trans. Biomed. Eng. 51:1176–1186, 2004.
Rosenbaum, D. S., and R. J. Cohen. Frequency based measures of atrial fibrillation in man. In: Engineering in Medicine and Biology Society, 1990. Proceedings of the Twelfth Annual International Conference of the IEEE, 1990, pp. 582–583.
Scargle, J. D. Studies in astronomical time series analysis. II—Statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263:835–853, 1982.
Schoellhamer, D. H. Singular spectrum analysis for time series with missing data. Geophys. Res. Lett. 28:3187–3190, 2001.
Slocum, J., A. Sahakian, and S. Swiryn. Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity. J. Electrocardiol. 25:1–8, 1992.
Stridh, M., A. Bollmann, B. Olsson, and L. Sornmo. Detection and feature extraction of atrial tachyarrhythmias. IEEE Eng. Med. Biol. Mag. 25:31–39, 2006.
Stridh, M., and L. Sornmo. Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation. IEEE Trans. Biomed. Eng. 48:105–111, 2001.
Stridh, M., L. Sornmo, C. Meurling, and S. Olsson. Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis. IEEE Trans. Biomed. Eng. 51:100–114, 2004.
Tai, C., S. Chen, A. Liu, W. Yu, Y. Ding, M. Chang, and T. Kao. Spectral analysis of chronic atrial fibrillation and its relation to minimal defibrillation energy. Pacing Clin. Electrophysiol. 25:1747–1751, 2002.
Tateno, K., and L. Glass. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of rr and δ rr intervals. Med. Biol. Eng. Comput. 39:664–671, 2001.
Tironi, D. A., R. Sassi, and L. T. Mainardi. Automated QT interval analysis on diagnostic electrocardiograms. IEEE Comput. Cardiol. 33:353–356, 2006.
Vautard, R., and M. Ghil. Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Physica D 35:395–424, 1989.
Vautard, R., P. Yiou, and M. Ghil. Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Physica D 58:95–126, 1992.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sassi, R., Corino, V.D.A. & Mainardi, L.T. Analysis of Surface Atrial Signals: Time Series with Missing Data?. Ann Biomed Eng 37, 2082–2092 (2009). https://doi.org/10.1007/s10439-009-9757-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10439-009-9757-3