Recent Advances in the Noninvasive Study of Atrial Conduction Defects Preceding Atrial Fibrillation

The P-wave represents the electrical activity in the electrocardiogram (ECG) associated with the heart\u27s atrial contraction. This wave has merited significant research efforts in recent years with the aim to characterize atrial depolarization from the ECG. Indeed, the alterations of the P-wave main time, frequency, and wavelet features have been widely studied to predict the onset of atrial fibrillation (AF), both spontaneously and after a specific treatment, such as pharmacological or electrical cardioversion, catheter ablation, as well as cardiac surgery. To this respect, the P-wave prolongation is today a clinically accepted marker of high risk of suffering AF. However, given the relatively low P-wave amplitude in the ECG, its analysis has been most widely carried out from signal-averaged ECG signals. Unfortunately, these kind of recordings are uncommon in routine clinical practice and, moreover, they obstruct the possibility of studying the information carried by each single P-wave as well as its variability over time. These limitations have motivated the recent development of the beat-to-beat P-wave analysis, which has proven to be very useful in revealing interesting information about the altered atrial conduction preceding the onset of AF. Within this context, the main goal of this chapter is to review the most recent advances reached by this kind of analysis in the noninvasive assessment of atrial conduction alterations. Thus, the chapter will introduce and discuss the existing methods of the beat-to-beat P-wave analysis and their application to predict the onset of AF as well as its advantages and disadvantages compared with the signal-averaged P-wave analysis

Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings

Background Atrial fibrillation (AF) is the most common supraventricular arrhythmia in the clinical practice, being the subject of intensive research. Methods The present work introduces two different Wavelet Transform (WT) applications to electrocardiogram (ECG) recordings of patients in AF. The first one predicts spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the prediction of electrical cardioversion (ECV) outcome in persistent AF patients. In both cases, the central tendency measure (CTM) from the first differences scatter plot was applied to the AF wavelet decomposition. In this way, the wavelet coefficients vector CTM associated to the AF frequency scale was used to assess how atrial fibrillatory (f) waves variability can be related to AF events. Results Structural changes into the f waves can be assessed by combining WT and CTM to reflect atrial activity organization variation. This fact can be used to predict organization-related events in AF. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity and accuracy were 100%, 91.67% and 96%, respectively. On the other hand, for ECV outcome prediction, 82.93% sensitivity, 90.91% specificity and 85.71% accuracy were obtained. Hence, CTM has reached the highest diagnostic ability as a single predictor published to date. Conclusions Results suggest that CTM can be considered as a promising tool to characterize non-invasive AF signals. In this sense, therapeutic interventions for the treatment of paroxysmal and persistent AF patients could be improved, thus, avoiding useless procedures and minimizing risks.This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation and PPII11-0194-8121 and PII1C09-0036-3237 from Junta de Comunidades de Castilla-La Mancha.Alcaraz, R.; Rieta Ibañez, JJ. (2012). Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings. BioMedical Engineering OnLine. 11(46):1-19. https://doi.org/10.1186/1475-925X-11-46S1191146Addison, P. S. (2005). Wavelet transforms and the ECG: a review. Physiological Measurement, 26(5), R155-R199. doi:10.1088/0967-3334/26/5/r01Miyasaka, Y., Barnes, M. E., Gersh, B. J., Cha, S. S., Bailey, K. R., Abhayaratna, W. P., … Tsang, T. S. M. (2006). Secular Trends in Incidence of Atrial Fibrillation in Olmsted County, Minnesota, 1980 to 2000, and Implications on the Projections for Future Prevalence. Circulation, 114(2), 119-125. doi:10.1161/circulationaha.105.595140Allessie, M. A., Konings, K., Kirchhof, C. J. H. J., & Wijffels, M. (1996). Electrophysiologic mechanisms of perpetuation of atrial fibrillation. The American Journal of Cardiology, 77(3), 10A-23A. doi:10.1016/s0002-9149(97)89114-xBollmann, A., Husser, D., Mainardi, L., Lombardi, F., Langley, P., Murray, A., … Sörnmo, L. (2006). Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications. EP Europace, 8(11), 911-926. doi:10.1093/europace/eul113GALL, N. P., & MURGATROYD, F. D. (2007). Electrical Cardioversion for AF?The State of the Art. Pacing and Clinical Electrophysiology, 30(4), 554-567. doi:10.1111/j.1540-8159.2007.00709.xAlcaraz, R., Hornero, F., & Rieta, J. J. (2010). Assessment of non-invasive time and frequency atrial fibrillation organization markers with unipolar atrial electrograms. Physiological Measurement, 32(1), 99-114. doi:10.1088/0967-3334/32/1/007Sih, H. J., Zipes, D. P., Berbari, E. J., & Olgin, J. E. (1999). A high-temporal resolution algorithm for quantifying organization during atrial fibrillation. IEEE Transactions on Biomedical Engineering, 46(4), 440-450. doi:10.1109/10.752941Goldberger, 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.e215Alcaraz, R., & Rieta, J. J. (2008). Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms. Physiological Measurement, 29(12), 1351-1369. doi:10.1088/0967-3334/29/12/001Stridh, 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.2003.820331Bollmann, A. (1999). Non-invasive assessment of fibrillatory activity in patients with paroxysmal and persistent atrial fibrillation using the Holter ECG. Cardiovascular Research, 44(1), 60-66. doi:10.1016/s0008-6363(99)00156-xCapucci, A., Biffi, M., Boriani, G., Ravelli, F., Nollo, G., Sabbatani, P., … Magnani, B. (1995). Dynamic Electrophysiological Behavior of Human Atria During Paroxysmal Atrial Fibrillation. Circulation, 92(5), 1193-1202. doi:10.1161/01.cir.92.5.1193Hornero, R., Abasolo, D., Jimeno, N., Sanchez, C. I., Poza, J., & Aboy, M. (2006). Variability, Regularity, and Complexity of Time Series Generated by Schizophrenic Patients and Control Subjects. IEEE Transactions on Biomedical Engineering, 53(2), 210-218. doi:10.1109/tbme.2005.862547Alcaraz, R., & Rieta, J. J. (2008). Wavelet bidomain sample entropy analysis to predict spontaneous termination of atrial fibrillation. Physiological Measurement, 29(1), 65-80. doi:10.1088/0967-3334/29/1/005Alcaraz, R., & Rieta, J. J. (2008). A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation. Medical & Biological Engineering & Computing, 46(7), 625-635. doi:10.1007/s11517-008-0348-5Brennan, M., Palaniswami, M., & Kamen, P. (2001). Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48(11), 1342-1347. doi:10.1109/10.959330Alcaraz, R., & Rieta, J. J. (2009). Sample entropy of the main atrial wave predicts spontaneous termination of paroxysmal atrial fibrillation. Medical Engineering & Physics, 31(8), 917-922. doi:10.1016/j.medengphy.2009.05.002ALCARAZ, R., HORNERO, F., & RIETA, J. J. (2011). Noninvasive Time and Frequency Predictors of Long-Standing Atrial Fibrillation Early Recurrence after Electrical Cardioversion. Pacing and Clinical Electrophysiology, 34(10), 1241-1250. doi:10.1111/j.1540-8159.2011.03125.xChen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1), 61-68. doi:10.1016/j.medengphy.2008.04.005Molina-Picó, A., Cuesta-Frau, D., Aboy, M., Crespo, C., Miró-Martínez, P., & Oltra-Crespo, S. (2011). Comparative study of approximate entropy and sample entropy robustness to spikes. Artificial Intelligence in Medicine, 53(2), 97-106. doi:10.1016/j.artmed.2011.06.007Alcaraz, R., & Rieta, J. J. (2009). Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion. Physiological Measurement, 30(5), 479-489. doi:10.1088/0967-3334/30/5/005Sun, R., & Wang, Y. (2008). Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot. Medical Engineering & Physics, 30(9), 1105-1111. doi:10.1016/j.medengphy.2008.01.008Alcaraz, R., Rieta, J. J., & Hornero, F. (2008). Caracterización no invasiva de la actividad auricular durante los instantes previos a la terminación de la fibrilación auricular paroxística. Revista Española de Cardiología, 61(2), 154-160. doi:10.1157/13116203Nilsson, F., Stridh, M., Bollmann, A., & Sörnmo, L. (2006). Predicting spontaneous termination of atrial fibrillation using the surface ECG. Medical Engineering & Physics, 28(8), 802-808. doi:10.1016/j.medengphy.2005.11.010Watson, J. N., Addison, P. S., Uchaipichat, N., Shah, A. S., & Grubb, N. R. (2007). Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients. Computers in Biology and Medicine, 37(4), 517-523. doi:10.1016/j.compbiomed.2006.08.003Holmqvist, F., Stridh, M., Waktare, J. E. P., Roijer, A., Sörnmo, L., Platonov, P. G., & Meurling, C. J. (2006). Atrial fibrillation signal organization predicts sinus rhythm maintenance in patients undergoing cardioversion of atrial fibrillation. EP Europace, 8(8), 559-565. doi:10.1093/europace/eul072ZOHAR, P., KOVACIC, M., BREZOCNIK, M., & PODBREGAR, M. (2005). Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling. Europace, 7(5), 500-507. doi:10.1016/j.eupc.2005.04.007VAN DEN BERG, M. P., VAN NOORD, T., BROUWER, J., HAAKSMA, J., VAN VELDHUISEN, D. J., CRIJNS, H. J. G. M., & VAN GELDER, I. C. (2004). Clustering of RR Intervals Predicts Effective Electrical Cardioversion for Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 15(9), 1027-1033. doi:10.1046/j.1540-8167.2004.03686.xAlcaraz, R., Rieta, J. J., & Hornero, F. (2009). Non-invasive atrial fibrillation organization follow-up under successive attempts of electrical cardioversion. Medical & Biological Engineering & Computing, 47(12), 1247-1255. doi:10.1007/s11517-009-0519-zZheng, H., & Wu, J. (2008). A Real-Time QRS Detector Based on Discrete Wavelet Transform and Cubic Spline Interpolation. Telemedicine and e-Health, 14(8), 809-815. doi:10.1089/tmj.2008.0073Pan, Y.-H., Wang, Y.-H., Liang, S.-F., & Lee, K.-T. (2011). Fast computation of sample entropy and approximate entropy in biomedicine. Computer Methods and Programs in Biomedicine, 104(3), 382-396. doi:10.1016/j.cmpb.2010.12.003Manis, G. (2008). Fast computation of approximate entropy. Computer Methods and Programs in Biomedicine, 91(1), 48-54. doi:10.1016/j.cmpb.2008.02.008Everett, T. H., Lai-Chow Kok, Vaughn, R. H., Moorman, R., & Haines, D. E. (2001). Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Transactions on Biomedical Engineering, 48(9), 969-978. doi:10.1109/10.942586HUSSER, D., STRIDH, M., CANNOM, D. S., BHANDARI, A. K., GIRSKY, M. J., KANG, S., … BOLLMANN, A. (2007). Validation and Clinical Application of Time-Frequency Analysis of Atrial Fibrillation Electrocardiograms. Journal of Cardiovascular Electrophysiology, 18(1), 41-46. doi:10.1111/j.1540-8167.2006.00683.

Application of wavelet entropy to predict atrial fibrillation progression from the surface ECG

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE) is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG) of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the electrical cardioversion (ECV) outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory ( f ) waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals.This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation and PPII11-0194-8121 and PII1C09-0036-3237 from Junta de Comunidades de Castilla-La Mancha.Alcaraz, R.; Rieta Ibañez, JJ. (2012). Application of wavelet entropy to predict atrial fibrillation progression from the surface ECG. Computational and Mathematical Methods in Medicine. 2012(245213):1-9. https://doi.org/10.1155/2012/245213S192012245213Fuster, V., Rydén, L. E., Cannom, D. S., Crijns, H. J., Curtis, A. B., … Ellenbogen, K. A. (2006). ACC/AHA/ESC 2006 Guidelines for the Management of Patients With Atrial Fibrillation. Circulation, 114(7). doi:10.1161/circulationaha.106.177292Gallagher, M. M., & Camm, J. (1998). Classification of atrial fibrillation. The American Journal of Cardiology, 82(7), 18N-28N. doi:10.1016/s0002-9149(98)00736-xKonings, K. T., Kirchhof, C. J., Smeets, J. R., Wellens, H. J., Penn, O. C., & Allessie, M. A. (1994). High-density mapping of electrically induced atrial fibrillation in humans. Circulation, 89(4), 1665-1680. doi:10.1161/01.cir.89.4.1665Allessie, M. A., Konings, K., Kirchhof, C. J. H. J., & Wijffels, M. (1996). Electrophysiologic mechanisms of perpetuation of atrial fibrillation. The American Journal of Cardiology, 77(3), 10A-23A. doi:10.1016/s0002-9149(97)89114-xSih, H. J., Zipes, D. P., Berbari, E. J., & Olgin, J. E. (1999). A high-temporal resolution algorithm for quantifying organization during atrial fibrillation. IEEE Transactions on Biomedical Engineering, 46(4), 440-450. doi:10.1109/10.752941TAKAHASHI, Y., SANDERS, P., JAIS, P., HOCINI, M., DUBOIS, R., ROTTER, M., … HAISSAGUERRE, M. (2006). Organization of Frequency Spectra of Atrial Fibrillation: Relevance to Radiofrequency Catheter Ablation. Journal of Cardiovascular Electrophysiology, 17(4), 382-388. doi:10.1111/j.1540-8167.2005.00414.xAlcaraz, R., Hornero, F., & Rieta, J. J. (2010). Assessment of non-invasive time and frequency atrial fibrillation organization markers with unipolar atrial electrograms. Physiological Measurement, 32(1), 99-114. doi:10.1088/0967-3334/32/1/007Rosso, O. A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schürmann, M., & Başar, E. (2001). Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of Neuroscience Methods, 105(1), 65-75. doi:10.1016/s0165-0270(00)00356-3Petrutiu, S., Ng, J., Nijm, G. M., Al-Angari, H., Swiryn, S., & Sahakian, A. V. (2006). Atrial fibrillation and waveform characterization. IEEE Engineering in Medicine and Biology Magazine, 25(6), 24-30. doi:10.1109/emb-m.2006.250505Al-Khatib, S. M., Wilkinson, W. E., Sanders, L. L., McCarthy, E. A., & Pritchett, E. L. C. (2000). Observations on the transition from intermittent to permanent atrial fibrillation. American Heart Journal, 140(1), 142-145. doi:10.1067/mhj.2000.107547GALL, N. P., & MURGATROYD, F. D. (2007). Electrical Cardioversion for AF?The State of the Art. Pacing and Clinical Electrophysiology, 30(4), 554-567. doi:10.1111/j.1540-8159.2007.00709.xGoldberger, 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.e215Bollmann, A., Husser, D., Mainardi, L., Lombardi, F., Langley, P., Murray, A., … Sörnmo, L. (2006). Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications. EP Europace, 8(11), 911-926. doi:10.1093/europace/eul113Alcaraz, R., & Rieta, J. J. (2008). Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms. Physiological Measurement, 29(12), 1351-1369. doi:10.1088/0967-3334/29/12/001Stridh, 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.2003.820331Alcaraz, R., & Rieta, J. J. (2008). Wavelet bidomain sample entropy analysis to predict spontaneous termination of atrial fibrillation. Physiological Measurement, 29(1), 65-80. doi:10.1088/0967-3334/29/1/005Alcaraz, R., & Rieta, J. J. (2008). A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation. Medical & Biological Engineering & Computing, 46(7), 625-635. doi:10.1007/s11517-008-0348-5Rafiee, J., Rafiee, M. A., Prause, N., & Schoen, M. P. (2011). Wavelet basis functions in biomedical signal processing. Expert Systems with Applications, 38(5), 6190-6201. doi:10.1016/j.eswa.2010.11.050Addison, P. S. (2005). Wavelet transforms and the ECG: a review. Physiological Measurement, 26(5), R155-R199. doi:10.1088/0967-3334/26/5/r01Alcaraz, R., & Rieta, J. J. (2009). Sample entropy of the main atrial wave predicts spontaneous termination of paroxysmal atrial fibrillation. Medical Engineering & Physics, 31(8), 917-922. doi:10.1016/j.medengphy.2009.05.002ALCARAZ, R., HORNERO, F., & RIETA, J. J. (2011). Noninvasive Time and Frequency Predictors of Long-Standing Atrial Fibrillation Early Recurrence after Electrical Cardioversion. Pacing and Clinical Electrophysiology, 34(10), 1241-1250. doi:10.1111/j.1540-8159.2011.03125.xAlcaraz, R., & Rieta, J. J. (2009). Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion. Physiological Measurement, 30(5), 479-489. doi:10.1088/0967-3334/30/5/005Alcaraz, R., Rieta, J. J., & Hornero, F. (2008). Caracterización no invasiva de la actividad auricular durante los instantes previos a la terminación de la fibrilación auricular paroxística. Revista Española de Cardiología, 61(2), 154-160. doi:10.1157/13116203Nilsson, F., Stridh, M., Bollmann, A., & Sörnmo, L. (2006). Predicting spontaneous termination of atrial fibrillation using the surface ECG. Medical Engineering & Physics, 28(8), 802-808. doi:10.1016/j.medengphy.2005.11.010Holmqvist, F. (2006). Atrial fibrillatory rate and sinus rhythm maintenance in patients undergoing cardioversion of persistent atrial fibrillation. European Heart Journal, 27(18), 2201-2207. doi:10.1093/eurheartj/ehl098PALINKAS, A. (2001). Clinical value of left atrial appendage flow velocity for predicting of cardioversion success in patients with non-valvular atrial fibrillation*1. European Heart Journal, 22(23), 2201-2208. doi:10.1053/euhj.2001.2891Holmqvist, F., Stridh, M., Waktare, J. E. P., Roijer, A., Sörnmo, L., Platonov, P. G., & Meurling, C. J. (2006). Atrial fibrillation signal organization predicts sinus rhythm maintenance in patients undergoing cardioversion of atrial fibrillation. EP Europace, 8(8), 559-565. doi:10.1093/europace/eul072Sun, R., & Wang, Y. (2008). Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot. Medical Engineering & Physics, 30(9), 1105-1111. doi:10.1016/j.medengphy.2008.01.008Watson, J. N., Addison, P. S., Uchaipichat, N., Shah, A. S., & Grubb, N. R. (2007). Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients. Computers in Biology and Medicine, 37(4), 517-523. doi:10.1016/j.compbiomed.2006.08.003ZOHAR, P., KOVACIC, M., BREZOCNIK, M., & PODBREGAR, M. (2005). Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling. Europace, 7(5), 500-507. doi:10.1016/j.eupc.2005.04.007Pan, Y.-H., Wang, Y.-H., Liang, S.-F., & Lee, K.-T. (2011). Fast computation of sample entropy and approximate entropy in biomedicine. Computer Methods and Programs in Biomedicine, 104(3), 382-396. doi:10.1016/j.cmpb.2010.12.003Calcagnini, G., Censi, F., Michelucci, A., & Bartolini, P. (2006). Descriptors of wavefront propagation. IEEE Engineering in Medicine and Biology Magazine, 25(6), 71-78. doi:10.1109/emb-m.2006.250510LAU, C.-P., & LOK, N.-S. (1997). A Comparison of Transvenous Atrial Defibrillation of Acute and Chronic Atrial Fibrillation and the Effect of Intravenous Sotalol on Human Atrial Defibrillation Threshold. Pacing and Clinical Electrophysiology, 20(10), 2442-2452. doi:10.1111/j.1540-8159.1997.tb06084.xNDREPEPA, G., KARCH, M. R., SCHNEIDER, M. A. E., WEYERBROCK, S., SCHREIECK, J., DEISENHOFER, I., … SCHMITT, C. (2002). Characterization of Paroxysmal and Persistent Atrial Fibrillation in the Human Left Atrium During Initiation and Sustained Episodes. Journal of Cardiovascular Electrophysiology, 13(6), 525-532. doi:10.1046/j.1540-8167.2002.00525.xSih, H. J., Zipes, D. P., Berbari, E. J., Adams, D. E., & Olgin, J. E. (2000). Differences in organization between acute and chronic atrial fibrillation in dogs. Journal of the American College of Cardiology, 36(3), 924-931. doi:10.1016/s0735-1097(00)00788-

The Contribution of Nonlinear Methods in the Understanding of Atrial Fibrillation

This work was supported by the projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 and PII1C09–0036–3237 from Junta de Comunidades de Castilla-La Mancha.Alcaraz Martínez, R.; Rieta Ibañez, JJ. (2013). The Contribution of Nonlinear Methods in the Understanding of Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 181-204. https://doi.org/10.5772/53407S18120

Generador de Funciones. Ondas, Amplitud y Frecuencia

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Fuente de Alimentación. Modo Paralelo

En el objeto de aprendizaje se describe el funcionamiento básico de una fuente de alimentación de laboratorio en modo paralelohttps://media.upv.es/player/?id=fb75d503-5924-492c-a7c8-720ce25c40e1Rieta Ibañez, JJ. (2012). Fuente de Alimentación. Modo Paralelo. http://hdl.handle.net/10251/1712

Generador de Funciones. Control externo de frecuencia

Descripción sobre el funcionamiento básico de un generador de señal de baja frecuencia y sus particularidades para controlar la frecuencia que genera desde el exterior.https://media.upv.es/player/?id=b0033189-eff7-4a71-aaae-3a73aad85984Rieta Ibañez, JJ. (2013). Generador de Funciones. Control externo de frecuencia. http://hdl.handle.net/10251/3150

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Fuente de Alimentación. Modo Independiente

En el objeto de aprendizaje se describe el funcionamiento básico de una fuente de alimentación de laboratorio funcionando en modo independiente.https://media.upv.es/player/?id=c77e1e63-88cd-4d5f-bf87-74490fefd7b8Rieta Ibañez, JJ. (2012). Fuente de Alimentación. Modo Independiente. http://hdl.handle.net/10251/1711