Estudio de los mecanismos de las arritmias cardiacas mediante modelado y procesado robusto digital de señal

Las arritmias cardiacas son alteraciones del funcionamiento eléctrico normal del corazón. Algunas, las arritmias patológicas, son potencialmente malignas y constituyen una de las principales causas de mortalidad en el mundo occidental. Hoy en día, el estudio de los mecanismos de las arritmias patológicas se asienta sobre dos ámbitos de investigación: de un lado, el análisis de señales cardiacas registradas mediante sistemas de captación con electrodos en estudios clínicos y/o experimentales, tales como los electrogramas intracavitarios y del electrocardiograma de superficie; de otro lado, el análisis de la dinámica del sustrato cardiaco a partir imágenes proporcionadas por sistemas de mapeo óptico en estudios experimentales in-vivo e in-vitro. A pesar de que estas dos aproximaciones se fundamentan en el análisis de la actividad eléctrica cardiaca, suelen recibir un trato independiente. Explorar la correspondencia entre los resultados proporcionados en cada campo por separado, permitiría definir nuevas estrategias para profundizar en el estudio de los mecanismos de las arritmias cardiacas. El objetivo principal de la presente Tesis Doctoral es construir el eje de una investigación integrada dedicada al estudio de los mecanismos involucrados la generación y perpetuación de las arritmias cardiacas. Nuestra metodología se basa en la combinación de herramientas de modelado y técnicas de procesado de señal para establecer una correspondencia directa entre la dinámica del sustrato cardiaco, las señales eléctricas registradas y la caracterización clínica mediante índices cardiacos extraídos a partir del procesado de las señales eléctricas cardiacas. En esta disertación desarrollamos los elementos que conforman un marco de investigación integrada siguiendo un enfoque incremental, desde la caracterización de la dinámica cardiaca a nivel celular, hasta el procesado de índices cardiacos. Concretamente, realizamos tres aportaciones principales. En primer lugar, analizamos a nivel celular los mecanismos de generación de arritmias inducidas por mutaciones congénitas a partir de un modelo detallado de célula cardiaca. Este análisis permite definir la dinámica un sustrato pro-arrítmico de gran interés clínico y experimental. En segundo lugar, abordamos la reconstrucción de la actividad eléctrica cardiaca a partir de señales eléctricas remotas mediante algoritmos de estimación basados en máquinas de vectores soporte (SVM). En este estudio, establecemos una relación bidireccional entre sustrato cardiaco y señal cardiaca. Por último, proponemos y evaluamos un método de detección de arritmias basado en índices cardiacos mediante el uso clasificadores SVM y técnicas de selección de características. Con este método es posible inferir, desde el punto de vista poblacional, conclusiones acerca de la dinámica del sustrato cardiaco. Cada una de estas aportaciones sienta las bases de una aproximación integrada al estudio de las arritmias cardiacas. La combinación de técnicas de procesado de señal y de herramientas de modelado constituye un marco de investigación beneficioso para esclarecer los mecanismos de las arritmias cardiacas.__________________________________Cardiac arrhythmias are disorders of the heartbeat due to cardiac electrical activity dysfunction. Among them, the so-called pathological arrhythmias can be life-threatening and constitute one of the leading causes of mortality in the western world. Nowadays, the study of the mechanisms of cardiac arrhythmias relies on two approaches. On the one hand, the analysis of cardiac electric signals registered by electrode systems in experimental and clinical studies. Examples of such cardiac signals are intracavitary electrograms and the surface electrocardiogram. On the other hand, the analysis of cardiac tissue dynamics based on optical mapping systems in in-vivo and in-vitro experimental studies. Nevertheless, these two approaches are usually treated independently and as a consequence the interaction among them is not fully exploited. The objective of this Thesis is to develop an integrated research framework to study the mechanisms involved in the initiation and perpetuation of cardiac arrhythmias. By combining computer modeling and signal processing techniques, we aim to establish a correspondence between cardiac tissue dynamics, cardiac electric signals and cardiac indices obtained from cardiac electric signals. Following an incremental approach, from cell dynamics to cardiac indices, we develop the elements of an integrated research framework. The main contributions of this dissertation can be summarized as follows. Firstly, we analyze at cellular level the mechanisms of cardiac arrhythmias induced by genetic mutations. For this purpose, we use a detailed computer model of ventricular cardiac cells to characterized the pro-arrhythmic dynamics of the mutant heart. Secondly, we propose a number of algorithms based on support vector machines (SVM) to reconstruct the cardiac electrical activity from remote cardiac electric signal. Therefore, a bidirectional link between cardiac tissue and cardiac signal is established. Finally, we develop a new method for detecting cardiac arrhythmia from cardiac indices by using a classification SVM and feature selection techniques. This study informs us about the relevance of cardiac indices in the characterization of cardiac tissue dynamics. The combination of computer modeling and signal processing techniques constitutes a suitable framework to elucidate the mechanisms of cardiac arrhythmi

Quality estimation of the electrocardiogram using cross-correlation among leads

Background Fast and accurate quality estimation of the electrocardiogram (ECG) signal is a relevant research topic that has attracted considerable interest in the scientific community, particularly due to its impact on tele-medicine monitoring systems, where the ECG is collected by untrained technicians. In recent years, a number of studies have addressed this topic, showing poor performance in discriminating between clinically acceptable and unacceptable ECG records. Methods This paper presents a novel, simple and accurate algorithm to estimate the quality of the 12-lead ECG by exploiting the structure of the cross-covariance matrix among different leads. Ideally, ECG signals from different leads should be highly correlated since they capture the same electrical activation process of the heart. However, in the presence of noise or artifacts the covariance among these signals will be affected. Eigenvalues of the ECG signals covariance matrix are fed into three different supervised binary classifiers. Results and conclusion The performance of these classifiers were evaluated using PhysioNet/CinC Challenge 2011 data. Our best quality classifier achieved an accuracy of 0.898 in the test set, while having a complexity well below the results of contestants who participated in the Challenge, thus making it suitable for implementation in current cellular devices.National Institute of General Medical Sciences (U.S.) (Grant R01GM104987)Spain (Research Grant TEC2013-46067-R)Spain (Research Grant TEC2013-48439-C4-1-R)Spain (Research Grant TEC2010-19263

Regularization Techniques for ECG Imaging during Atrial Fibrillation: A Computational Study

The inverse problem of electrocardiography is usually analyzed during stationary rhythms. However, the performance of the regularization methods under fibrillatory conditions has not been fully studied. In this work, we assessed different regularization techniques during atrial fibrillation (AF) for estimating four target parameters, namely, epicardial potentials, dominant frequency (DF), phase maps, and singularity point (SP) location. We use a realistic mathematical model of atria and torso anatomy with three different electrical activity patterns (i.e., sinus rhythm, simple AF, and complex AF). Body surface potentials (BSP) were simulated using Boundary Element Method and corrupted with white Gaussian noise of different powers. Noisy BSPs were used to obtain the epicardial potentials on the atrial surface, using 14 different regularization techniques. DF, phase maps, and SP location were computed from estimated epicardial potentials. Inverse solutions were evaluated using a set of performance metrics adapted to each clinical target. For the case of SP location, an assessment methodology based on the spatial mass function of the SP location, and four spatial error metrics was proposed. The role of the regularization parameter for Tikhonov-based methods, and the effect of noise level and imperfections in the knowledge of the transfer matrix were also addressed. Results showed that the Bayes maximum-a-posteriori method clearly outperforms the rest of the techniques but requires a priori information about the epicardial potentials. Among the purely non-invasive techniques. Tikhonov-based methods performed as well as more complex techniques in realistic fibrillatory conditions, with a slight gain between 0.02 and 0.2 in terms of the correlation coefficient. Also, the use of a constant regularization parameter may be advisable since the performance was similar to that obtained with a variable parameter (indeed there was no difference for the zero-order Tikhonov method in complex fibrillatory conditions). Regarding the different targets. DF and SP location estimation were more robust with respect to pattern complexity and noise, and most algorithms provided a reasonable estimation of these parameters, even when the epicardial potentials estimation was inaccurate. Finally, the proposed evaluation procedure and metrics represent a suitable framework for techniques benchmarking and provide useful insights for the clinical practice.This work has been partially supported by TEC2013-46067-R (Ministerio de Economia y Competitividad, Spanish Government).Figuera C; Suárez Gutiérrez V; Hernández-Romero, I.; Rodrigo Bort, M.; Liberos Mascarell, A.; Atienza, F.; Guillem Sánchez, MS.... (2016). Regularization Techniques for ECG Imaging during Atrial Fibrillation: A Computational Study. Frontiers in Physiology. 7(466):1-17. https://doi.org/10.3389/fphys.2016.00466S117746

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.This study was supported by the Ministerio de Economía, Industria y Competitividad, Gobierno de España (ES) (TEC-2015-64678-R) to UI and EA and by Euskal Herriko Unibertsitatea (ES) (GIU17/031) to UI and EA. The funders, Tecnalia Research and Innovation and Banco Bilbao Vizcaya Argentaria (BBVA), provided support in the form of salaries for authors AP, AA, FAA, CF, EG, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section

Magnetic resonance imaging based signal intensity mapping predicts appropriate therapies after prophylactic ventricular tachycardia substrate ablation in patients with previous myocardial infarction and secondary prevention implantable cardioverter-defibrillator implantation

The aim of this study was to determine the capability of ceMRI based signal intensity (SI) mapping to predict appropriate ICD therapies after PVTSA

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s

The Rise and Fall of "Respectable" Spanish Liberalism, 1808-1923: An Explanatory Framework

The article focuses on the reasons behind both the consolidation of what I have termed “respectable” liberalism between the 1830s and the 1840s and its subsequent decline and fall between 1900 and 1923. In understanding both processes I study the links established between “respectable” liberals and propertied elites, the monarchy, and the Church. In the first phase these links served to consolidate the liberal polity. However, they also meant that many tenets of liberal ideology were compromised. Free elections were undermined by the operation of caciquismo, monarchs established a powerful position, and despite the Church hierarchy working with liberalism, the doctrine espoused by much of the Church was still shaped by the Counter-Reformation. Hence, “respectable” liberalism failed to achieve a popular social base. And the liberal order was increasingly denigrated as part of the corrupt “oligarchy” that ruled Spain. Worse still, between 1916 and 1923 the Church, monarch, and the propertied elite increasingly abandoned the liberal Monarchist Restoration. Hence when General Primo de Rivera launched his coup the rug was pulled from under the liberals’ feet and there was no one to cushion the fall

The Mars Environmental Dynamics Analyzer, MEDA. A Suite of Environmental Sensors for the Mars 2020 Mission

86 pags, 49 figs, 24 tabsNASA's Mars 2020 (M2020) rover mission includes a suite of sensors to monitor current environmental conditions near the surface of Mars and to constrain bulk aerosol properties from changes in atmospheric radiation at the surface. The Mars Environmental Dynamics Analyzer (MEDA) consists of a set of meteorological sensors including wind sensor, a barometer, a relative humidity sensor, a set of 5 thermocouples to measure atmospheric temperature at ∼1.5 m and ∼0.5 m above the surface, a set of thermopiles to characterize the thermal IR brightness temperatures of the surface and the lower atmosphere. MEDA adds a radiation and dust sensor to monitor the optical atmospheric properties that can be used to infer bulk aerosol physical properties such as particle size distribution, non-sphericity, and concentration. The MEDA package and its scientific purpose are described in this document as well as how it responded to the calibration tests and how it helps prepare for the human exploration of Mars. A comparison is also presented to previous environmental monitoring payloads landed on Mars on the Viking, Pathfinder, Phoenix, MSL, and InSight spacecraft.This work has been funded by the Spanish Ministry of Economy and Competitiveness, through the projects No. ESP2014-54256-C4-1-R (also -2-R, -3-R and -4-R) and AYA2015-65041-P; Ministry of Science, Innovation and Universities, projects No. ESP2016-79612-C3-1-R (also -2-R and -3-R), ESP2016-80320-C2-1-R, RTI2018-098728-B-C31 (also -C32 and -C33) and RTI2018-099825-B-C31; Instituto Nacional de Tecnica Aeroespacial; Ministry of Science and Innovation's Centre for the Development of Industrial Technology; Grupos Gobierno Vasco IT1366-19; and European Research Council Consolidator Grant no 818602.Peer reviewe

Inverse Problem of Electrocardiography: estimating the location of cardiac isquemia in a 3D geometry

The inverse problem in cardiology (IPC) has been formulated in different ways in order to non invasively obtain valuable infor-mations about the heart condition. Most of the formulations solve the IPC under a quasistatic assumption neglecting the dynamic behavior of the electrical wave propagation in the heart. In this work we take into account this dynamic behavior by constraining the cost function with the monodomain model. We use an iterative algorithm combined with a level set formulation allowing us to localize an ischemic region in the heart. The method has been presented by Alvarez et al in [1] and [4], in which the authors developed a method for localize ischemic regions using a simple phenomenological model in a 2D cardiac tissue. In this work, we analyze the performance of this method in different 3D geometries. The inverse procedure exploits the spatiotemporal correlations contained in the observed data, which is formulated as a parametric adjust of a mathematical model that minimizes the misfit between the simulated and the observed data. We start by testing this method on two concentric spheres and then analyze the performance in a 3D real anatomical geometry. Both for analytical and real life geometries, numerical results show that using this algorithm we are capable of identifying the position and, in most of the cases, approximate the size of the ischemic regions

An Analytical Model for the Effects of the Spatial Resolution of Electrode Systems on the Spectrum of Cardiac Signals

It has been suggested that the spatiotemporal characteristics of complex cardiac arrhythmias can be extracted from the spectrum of cardiac signals. However, the analysis of simple bioelectric models indicates that the spectrum of cardiac signals can be affected by the spatial resolution of the electrode system. In this study, we derive exact measurement transfer functions relating the spectrum of cardiac signals to the spatiotemporal dynamics of cardiac sources and estimate their bandwidths. The analysis of the measurement transfer bandwidths for dynamics with different degrees of spatiotemporal correlation shows that as the spatial resolution decreases, the bandwidth of the measurement transfer function decreases until it reaches a constant value. Moreover, this transition from decreasing to constant values is determined by the degree of spatiotemporal correlation of the underlying cardiac source. Motivated by our analytical results, we investigate in a realistic computer simulation environment the impact of additive noise on the accuracy of body-surface dominant frequency (DF) maps. Our simulation results show that meaningful DF values are obtained on those locations where the analytical measurement transfer bandwidth is wide. These findings suggest that the accuracy of body-surface DF maps can be limited by the low spatial resolution of body-surface electrode systems