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

Diseño de micro y macro espejos de actuación electrostática

En este trabajo se presenta el diseño de micro y macro espejos de actuación electrostática. Debido a la complejidad de los fenómenos que intervienen en el dispositivo, en este capítulo se presta especial atención al diseño de los actuadores electrostáticos tanto micro como macroscópicos, y se deja como trabajo futuro el análisis de las propiedades ópticas de los dispositivos aquí descritos. Las dimensiones del microespejo se encuentran en el rango de los micrómetros, mientras que las del macroespejo se encuentran en el rango de los milímetros. Existe un factor de escalamiento de 1:250 entre las dimensiones de los espejos que se espera incida en este mismo orden de magnitud en los fenómenos electromecánicos en la micro y macro escala. Se detalla el diseño electro-mecánico y se explica su principio de funcionamiento. Se proporcionan modelos matemáticos y simulaciones numéricas que predicen el comportamiento de los principales elementos electromecánicos de los espejos. Se discuten las implicaciones en el mecanismo de actuación como resultado del escalamiento en las dimensiones.ITESO, A.C.Universidad de Guadalajar

Entorno para la especificación, validación y generación de código para arduino

[EN] Web tool with which to specify, validate, simulate and generate code for Arduino boards.[ES] Herramienta web con la cual poder especificar, validar, simular y generar código para placas Arduino.García Alcaraz, R. (2014). Entorno para la especificación, validación y generación de código para arduino. http://hdl.handle.net/10251/50784Archivo delegad

A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs

[EN] A broad variety of algorithms for detection and classification of rhythm and morphology abnormalities in ECG recordings have been proposed in the last years. Although some of them have reported very promising results, they have been mostly validated on short and non-public datasets, thus making their comparison extremely difficult. PhysioNet/CinC Challenge 2020 provides an interesting opportunity to compare these and other algorithms on a wide set of ECG recordings. The present model was created by ¿ELBIT¿ team. The algorithm is based on deep learning, and the segmentation of all beats in the 12-lead ECG recording, generating a new signal for each one by concatenating sequentially the information found in each lead. The resulting signal is then transformed into a 2- D image through a continuous Wavelet transform and inputted to a convolutional neural network. According to the competition guidelines, classification results were evaluated in terms of a class-weighted F-score (Fß) and a generalization of the Jaccard measure (Gß). In average for all training signals, these metrics were 0.933 and 0.811, respectively. Regarding validation on the testing set from the first phase of the challenge, mean values for both performance indices were 0.654 and 0.372, respectivelyThis research has been supported by the grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744Huerta, A.; Martinez-Rodrigo, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.305S1

ECG Quality Assessment via Deep Learning and Data Augmentation

[EN] Quality assessment of ECG signals acquired with wearable devices is essential to avoid misdiagnosis of some cardiac disorders. For that purpose, novel deep learning algorithms have been recently proposed. However, training of these methods require large amount of data and public databases with annotated ECG samples are limited. Hence, the present work aims at validating the usefulness of a well-known data augmentation approach in this context of ECG quality assessment. Precisely, classification between high- and low-quality ECG excerpts achieved by a common convolutional neural network (CNN) trained on two databases has been compared. On the one hand, 2,000 5 second-length ECG excerpts were initially selected from a freely available database. Half of the segments were extracted from noisy ECG recordings and the other half from high-quality signals. On the other hand, using a data augmentation approach based on time-scale modification, noise addition, and pitch shifting of the original noisy ECG experts, 1,000 additional low-quality intervals were generated. These surrogate noisy signals and the original highquality ones formed the second dataset. The results for both cases were compared using a McNemar test and no statistically significant differences were noticed, thus suggesting that the synthesized noisy signals could be used for reliable training of CNN-based ECG quality indices.Huerta, Á.; Martínez-Rodrigo, A.; Rieta, JJ.; Alcaraz, R. (2021). ECG Quality Assessment via Deep Learning and Data Augmentation. 1-4. https://doi.org/10.22489/CinC.2021.2431

Membrane parallelism for discrete Morse theory applied to digital images

In this paper, we propose a bio-inspired membrane computational framework for constructing discrete Morse complexes for binary digital images. Our approach is based on the discrete Morse theory and we work with cubical complexes. As example, a parallel algorithm for computing homology groups of binary 3D digital images is designed

Partial Reconfiguration of Control Systems using Petri Nets Structural Redundancy

This paper deals with the partial reconfiguration of the discrete control systems due to resource failures using the structural redundancy of the global system model. The approach herein proposed introduces a new subclass of Interpreted Petri Nets (), named Interpreted Machines with Resources (), allowing representing both the behaviour of a system and the resource allocation. Based on this model, an efficient reconfiguration algorithm is proposed; it is based on finding the set of all redundant sequences using alternative resources. The advantages of this structural reconfiguration method are: (1) it provides minimal reconfiguration to the system control assuring the properties of the original control system, (2) since the model includes resource allocation, it can be applied to a variety of systems such as Business Processes, and FPGAs, among others, (3) it takes advantage of the implied features of Petri net models, such as structural analysis and graphical visualization of the system and control. The method is illustrated through a case study that deals with a manufacturing system controller, which includes both alternative resources and operation sequencesITESO, A.C.CINVESTA

Las redes de Petri en la paralelización eficiente de aplicaciones: caso de uso

En este trabajo se presenta el método basado en modelos de Redes de Petri para el análisis y paralelización eficiente de aplicaciones programadas con un paradigma secuencial. Primeramente, se realiza el modelo de la aplicación secuencial. Enseguida, se analizan las partes paralelizables, y se presenta un modelo en Red de Petri de la aplicación paralelizada. A partir del modelo en Red de Petri, se realiza la verificación de la construcción del modelo y se analiza de manera informal la relación de los P-Invariantes con la paralelización del modelo. Finalmente, se realiza una comparación del tiempo de cómputo entre el paradigma secuencial y el paralelo. Se utiliza la multiplicación de matrices como caso de estudio y se reportan los resultados experimentales.Universidad de Guadalajar

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). 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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-