Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

Abstract

[EN] Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f -waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f -wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f -wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. Main results: Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates comparison of the performance of different f -wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f -wave signals.This work was supported by project DPI2017-83952-C3 of the Spanish Ministry of Economy, Industry and Competitiveness, project SBPLY/17/180501/000411 of the Junta de Comunidades de Castilla-La Mancha, Grant 'Jose Castillejo' (CAS17/00436) from the Spanish Ministry of Education, Culture and Sport, Grant No. BEST/2017/028 from the Education, Research, Culture and Sports Department of Generalitat Valenciana, European Regional Development Fund, and Grant No. 03382/2016 from the Swedish Research Council.Alcaraz, R.; Sornmo, L.; Rieta, JJ. (2019). Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG. Physiological Measurement. 40(7):1-11. https://doi.org/10.1088/1361-6579/ab2b17S111407Chugh, S. S., Roth, G. A., Gillum, R. F., & Mensah, G. A. (2014). Global Burden of Atrial Fibrillation in Developed and Developing Nations. Global Heart, 9(1), 113. doi:10.1016/j.gheart.2014.01.004Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T., & Liu, X. (2013). Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population. The American Journal of Cardiology, 112(8), 1142-1147. doi:10.1016/j.amjcard.2013.05.063Cuculich, P. S., Wang, Y., Lindsay, B. D., Faddis, M. N., Schuessler, R. B., Damiano, R. J., … Rudy, Y. (2010). Noninvasive Characterization of Epicardial Activation in Humans With Diverse Atrial Fibrillation Patterns. Circulation, 122(14), 1364-1372. doi:10.1161/circulationaha.110.945709Dai, H., Jiang, S., & Li, Y. (2013). Atrial activity extraction from single lead ECG recordings: Evaluation of two novel methods. Computers in Biology and Medicine, 43(3), 176-183. doi:10.1016/j.compbiomed.2012.12.005Donoso, F. I., Figueroa, R. L., Lecannelier, E. A., Pino, E. J., & Rojas, A. J. (2013). Atrial activity selection for atrial fibrillation ECG recordings. Computers in Biology and Medicine, 43(10), 1628-1636. doi:10.1016/j.compbiomed.2013.08.002Fauchier, L., Villejoubert, O., Clementy, N., Bernard, A., Pierre, B., Angoulvant, D., … Lip, G. Y. H. (2016). Causes of Death and Influencing Factors in Patients with Atrial Fibrillation. The American Journal of Medicine, 129(12), 1278-1287. doi:10.1016/j.amjmed.2016.06.045Fujiki, A., Sakabe, M., Nishida, K., Mizumaki, K., & Inoue, H. (2003). Role of Fibrillation Cycle Length in Spontaneous and Drug-Induced Termination of Human Atrial Fibrillation. Circulation Journal, 67(5), 391-395. doi:10.1253/circj.67.391Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). doi:10.1161/01.cir.101.23.e215Roonizi, E. K., & Sassi, R. (2017). An Extended Bayesian Framework for Atrial and Ventricular Activity Separation in Atrial Fibrillation. IEEE Journal of Biomedical and Health Informatics, 21(6), 1573-1580. doi:10.1109/jbhi.2016.2625338Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280Langley, P. (2015). Wavelet Entropy as a Measure of Ventricular Beat Suppression from the Electrocardiogram in Atrial Fibrillation. Entropy, 17(12), 6397-6411. doi:10.3390/e17096397Langley, P., Rieta, J. J., Stridh, M., Millet, J., Sornmo, L., & Murray, A. (2006). Comparison of Atrial Signal Extraction Algorithms in 12-Lead ECGs With Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, 53(2), 343-346. doi:10.1109/tbme.2005.862567Lee, J., Song, M., Shin, D., & Lee, K. (2012). Event synchronous adaptive filter based atrial activity estimation in single-lead atrial fibrillation electrocardiograms. Medical & Biological Engineering & Computing, 50(8), 801-811. doi:10.1007/s11517-012-0931-7Lemay, M., Vesin, J.-M., van Oosterom, A., Jacquemet, V., & Kappenberger, L. (2007). Cancellation of Ventricular Activity in the ECG: Evaluation of Novel and Existing Methods. IEEE Transactions on Biomedical Engineering, 54(3), 542-546. doi:10.1109/tbme.2006.888835Llinares, R., Igual, J., & Miró-Borrás, J. (2010). A fixed point algorithm for extracting the atrial activity in the frequency domain. Computers in Biology and Medicine, 40(11-12), 943-949. doi:10.1016/j.compbiomed.2010.10.006Malik, J., Reed, N., Wang, C.-L., & Wu, H. (2017). Single-lead f-wave extraction using diffusion geometry. Physiological Measurement, 38(7), 1310-1334. doi:10.1088/1361-6579/aa707cMateo, J., & Joaquín Rieta, J. (2013). Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation. Computers in Biology and Medicine, 43(2), 154-163. doi:10.1016/j.compbiomed.2012.11.007McSharry, P. E., Clifford, G. D., Tarassenko, L., & Smith, L. A. (2003). A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering, 50(3), 289-294. doi:10.1109/tbme.2003.808805Nault, I., Lellouche, N., Matsuo, S., Knecht, S., Wright, M., Lim, K.-T., … Haïssaguerre, M. (2009). Clinical value of fibrillatory wave amplitude on surface ECG in patients with persistent atrial fibrillation. Journal of Interventional Cardiac Electrophysiology, 26(1), 11-19. doi:10.1007/s10840-009-9398-3Petrenas, A., Marozas, V., Sološenko, A., Kubilius, R., Skibarkiene, J., Oster, J., & Sörnmo, L. (2017). Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes. Physiological Measurement, 38(11), 2058-2080. doi:10.1088/1361-6579/aa9153Petrenas, A., Marozas, V., Sornmo, L., & Lukosevicius, A. (2012). An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation. IEEE Transactions on Biomedical Engineering, 59(10), 2950-2957. doi:10.1109/tbme.2012.2212895Platonov, P. G., Corino, V. D. A., Seifert, M., Holmqvist, F., & Sornmo, L. (2014). Atrial fibrillatory rate in the clinical context: natural course and prediction of intervention outcome. Europace, 16(suppl 4), iv110-iv119. doi:10.1093/europace/euu249Sassi, R., Corino, V. D. A., & Mainardi, L. T. (2009). Analysis of Surface Atrial Signals: Time Series with Missing Data? Annals of Biomedical Engineering, 37(10), 2082-2092. doi:10.1007/s10439-009-9757-3Schotten, U., Dobrev, D., Platonov, P. G., Kottkamp, H., & Hindricks, G. (2016). Current controversies in determining the main mechanisms of atrial fibrillation. Journal of Internal Medicine, 279(5), 428-438. doi:10.1111/joim.12492Shah, D., Yamane, T., Choi, K.-J., & Haissaguerre, M. (2004). QRS Subtraction and the ECG Analysis of Atrial Ectopics. Annals of Noninvasive Electrocardiology, 9(4), 389-398. doi:10.1111/j.1542-474x.2004.94555.xSörnmo, L., Alcaraz, R., Laguna, P., & Rieta, J. J. (2018). Characterization of f Waves. Series in BioEngineering, 221-279. doi:10.1007/978-3-319-68515-1_6Sörnmo, L., Petrėnas, A., Laguna, P., & Marozas, V. (2018). Extraction of f Waves. Series in BioEngineering, 137-220. doi:10.1007/978-3-319-68515-1_5Sterling, M., Huang, D. T., & Ghoraani, B. (2015). Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation. Computational and Mathematical Methods in Medicine, 2015, 1-10. doi:10.1155/2015/527815Stridh, M., & Sommo, L. (2001). Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation. IEEE Transactions on Biomedical Engineering, 48(1), 105-111. doi:10.1109/10.900266Stridh, M., Sornmo, L., Meurling, C. J., & Olsson, S. B. (2004). Sequential Characterization of Atrial Tachyarrhythmias Based on ECG Time-Frequency Analysis. IEEE Transactions on Biomedical Engineering, 51(1), 100-114. doi:10.1109/tbme.2003.820331Wang, Y., & Jiang, Y. (2008). ISAR Imaging of Rotating Target with Equal Changing Acceleration Based on the Cubic Phase Function. EURASIP Journal on Advances in Signal Processing, 2008, 1-5. doi:10.1155/2008/49138