Cardiovascular function and ballistocardiogram: a relationship interpreted via mathematical modeling

Objective: to develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG). Methods: a closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed. Results: simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition. Conclusion: the proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology. Significance: this work provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present study considers an average human body and can potentially be extended to include variability among individuals

Uncertainty Quantification and Reduction in Cardiac Electrophysiological Imaging

Cardiac electrophysiological (EP) imaging involves solving an inverse problem that infers cardiac electrical activity from body-surface electrocardiography data on a physical domain defined by the body torso. To avoid unreasonable solutions that may fit the data, this inference is often guided by data-independent prior assumptions about different properties of cardiac electrical sources as well as the physical domain. However, these prior assumptions may involve errors and uncertainties that could affect the inference accuracy. For example, common prior assumptions on the source properties, such as fixed spatial and/or temporal smoothness or sparseness assumptions, may not necessarily match the true source property at different conditions, leading to uncertainties in the inference. Furthermore, prior assumptions on the physical domain, such as the anatomy and tissue conductivity of different organs in the thorax model, represent an approximation of the physical domain, introducing errors to the inference. To determine the robustness of the EP imaging systems for future clinical practice, it is important to identify these errors/uncertainties and assess their impact on the solution. This dissertation focuses on the quantification and reduction of the impact of uncertainties caused by prior assumptions/models on cardiac source properties as well as anatomical modeling uncertainties on the EP imaging solution. To assess the effect of fixed prior assumptions/models about cardiac source properties on the solution of EP imaging, we propose a novel yet simple Lp-norm regularization method for volumetric cardiac EP imaging. This study reports the necessity of an adaptive prior model (rather than fixed model) for constraining the complex spatiotemporally changing properties of the cardiac sources. We then propose a multiple-model Bayesian approach to cardiac EP imaging that employs a continuous combination of prior models, each re-effecting a specific spatial property for volumetric sources. The 3D source estimation is then obtained as a weighted combination of solutions across all models. Including a continuous combination of prior models, our proposed method reduces the chance of mismatch between prior models and true source properties, which in turn enhances the robustness of the EP imaging solution. To quantify the impact of anatomical modeling uncertainties on the EP imaging solution, we propose a systematic statistical framework. Founded based on statistical shape modeling and unscented transform, our method quantifies anatomical modeling uncertainties and establish their relation to the EP imaging solution. Applied on anatomical models generated from different image resolutions and different segmentations, it reports the robustness of EP imaging solution to these anatomical shape-detail variations. We then propose a simplified anatomical model for the heart that only incorporates certain subject-specific anatomical parameters, while discarding local shape details. Exploiting less resources and processing for successful EP imaging, this simplified model provides a simple clinically-compatible anatomical modeling experience for EP imaging systems. Different components of our proposed methods are validated through a comprehensive set of synthetic and real-data experiments, including various typical pathological conditions and/or diagnostic procedures, such as myocardial infarction and pacing. Overall, the methods presented in this dissertation for the quantification and reduction of uncertainties in cardiac EP imaging enhance the robustness of EP imaging, helping to close the gap between EP imaging in research and its clinical application

A personalized real-time virtual model of whole heart electrophysiology

Computer models capable of representing the intrinsic personal electrophysiology (EP) of the heart in silico are termed virtual heart technologies. When anatomy and EP are tailored to individual patients within the model, such technologies are promising clinical and industrial tools. Regardless of their vast potential, few virtual technologies simulating the entire organ-scale EP of all four-chambers of the heart have been reported and widespread clinical use is limited due to high computational costs and difficulty in validation. We thus report on the development of a novel virtual technology representing the electrophysiology of all four-chambers of the heart aiming to overcome these limitations. In our previous work, a model of ventricular EP embedded in a torso was constructed from clinical magnetic resonance image (MRI) data and personalized according to the measured 12 lead electrocardiogram (ECG) of a single subject under normal sinus rhythm. This model is then expanded upon to include whole heart EP and a detailed representation of the His-Purkinje system (HPS). To test the capacities of the personalized virtual heart technology to replicate standard clinical morphological ECG features under such conditions, bundle branch blocks within both the right and the left ventricles under two different conduction velocity settings are modeled alongside sinus rhythm. To ensure clinical viability, model generation was completely automated and simulations were performed using an efficient real-time cardiac EP simulator. Close correspondence between the measured and simulated 12 lead ECG was observed under normal sinus conditions and all simulated bundle branch blocks manifested relevant clinical morphological features

Personalized noninvasive imaging of volumetric cardiac electrophysiology

Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia

MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

Aims:Patient-to-patient anatomical differences are an important source of variability in the electrocardiogram, and they may compromise the identification of pathological electrophysiological abnormalities. This study aims at quantifying the contribution of variability in ventricular and torso anatomies to differences in QRS complexes of the 12-lead ECG using computer simulations. Methods:A computational pipeline is presented that enables computer simulations using human torso/biventricular anatomically based electrophysiological models from clinically standard magnetic resonance imaging (MRI). The ventricular model includes membrane kinetics represented by the biophysically detailed O’Hara Rudy model modified for tissue heterogeneity and includes fiber orientation based on the Streeter rule. A population of 265 torso/biventricular models was generated by combining ventricular and torso anatomies obtained from clinically standard MRIs, augmented with a statistical shape model of the body. 12-lead ECGs were simulated on the 265 human torso/biventricular electrophysiology models, and QRS morphology,duration and amplitude were quantified in each ECG lead for each of the human torso-biventricular models. Results:QRS morphologies in limb leads are mainly determined by ventricular anatomy,while in the precordial leads, and especially V1 to V4, they are determined by heart position within the torso. Differences in ventricular orientation within the torso can explain morphological variability from monophasic to biphasic QRS complexes. QRS duration ismainly influenced by myocardial volume, while it is hardly affected by the torso anatomyor position. An average increase of 0.12±0.05 ms in QRS duration is obtained for eachcm3of myocardial volume across all the leads while it hardly changed due to changes in torso volume. Conclusion:Computer simulations using populations of human torso/biventricular models based on clinical MRI enable quantification of anatomical causes of variability in the QRS complex of the 12-lead ECG. The human models presented also pave theway toward their use as testbeds in silico clinical trial

Computational modelling of the human heart and multiscale simulation of its electrophysiological activity aimed at the treatment of cardiac arrhythmias related to ischaemia and Infarction

[ES] Las enfermedades cardiovasculares constituyen la principal causa de morbilidad y mortalidad a nivel mundial, causando en torno a 18 millones de muertes cada año. De entre ellas, la más común es la enfermedad isquémica cardíaca, habitualmente denominada como infarto de miocardio (IM). Tras superar un IM, un considerable número de pacientes desarrollan taquicardias ventriculares (TV) potencialmente mortales durante la fase crónica del IM, es decir, semanas, meses o incluso años después la fase aguda inicial. Este tipo concreto de TV normalmente se origina por una reentrada a través de canales de conducción (CC), filamentos de miocardio superviviente que atraviesan la cicatriz del infarto fibrosa y no conductora. Cuando los fármacos anti-arrítmicos resultan incapaces de evitar episodios recurrentes de TV, la ablación por radiofrecuencia (ARF), un procedimiento mínimamente invasivo realizado mediante cateterismo en el laboratorio de electrofisiología (EF), se usa habitualmente para interrumpir de manera permanente la propagación eléctrica a través de los CCs responsables de la TV. Sin embargo, además de ser invasivo, arriesgado y requerir mucho tiempo, en casos de TVs relacionadas con IM crónico, hasta un 50% de los pacientes continúa padeciendo episodios recurrentes de TV tras el procedimiento de ARF. Por tanto, existe la necesidad de desarrollar nuevas estrategias pre-procedimiento para mejorar la planificación de la ARF y, de ese modo, aumentar esta tasa de éxito relativamente baja. En primer lugar, realizamos una revisión exhaustiva de la literatura referente a los modelos cardiacos 3D existentes, con el fin de obtener un profundo conocimiento de sus principales características y los métodos usados en su construcción, con especial atención sobre los modelos orientados a simulación de EF cardíaca. Luego, usando datos clínicos de un paciente con historial de TV relacionada con infarto, diseñamos e implementamos una serie de estrategias y metodologías para (1) generar modelos computacionales 3D específicos de paciente de ventrículos infartados que puedan usarse para realizar simulaciones de EF cardíaca a nivel de órgano, incluyendo la cicatriz del infarto y la región circundante conocida como zona de borde (ZB); (2) construir modelos 3D de torso que permitan la obtención del ECG simulado; y (3) llevar a cabo estudios in-silico de EF personalizados y pre-procedimiento, tratando de replicar los verdaderos estudios de EF realizados en el laboratorio de EF antes de la ablación. La finalidad de estas metodologías es la de localizar los CCs en el modelo ventricular 3D para ayudar a definir los objetivos de ablación óptimos para el procedimiento de ARF. Por último, realizamos el estudio retrospectivo por simulación de un caso, en el que logramos inducir la TV reentrante relacionada con el infarto usando diferentes configuraciones de modelado para la ZB. Validamos nuestros resultados mediante la reproducción, con una precisión razonable, del ECG del paciente en TV, así como en ritmo sinusal a partir de los mapas de activación endocárdica obtenidos invasivamente mediante sistemas de mapeado electroanatómico en este último caso. Esto permitió encontrar la ubicación y analizar las características del CC responsable de la TV clínica. Cabe destacar que dicho estudio in-silico de EF podría haberse efectuado antes del procedimiento de ARF, puesto que nuestro planteamiento está completamente basado en datos clínicos no invasivos adquiridos antes de la intervención real. Estos resultados confirman la viabilidad de la realización de estudios in-silico de EF personalizados y pre-procedimiento de utilidad, así como el potencial del abordaje propuesto para llegar a ser en un futuro una herramienta de apoyo para la planificación de la ARF en casos de TVs reentrantes relacionadas con infarto. No obstante, la metodología propuesta requiere de notables mejoras y validación por medio de es[CA] Les malalties cardiovasculars constitueixen la principal causa de morbiditat i mortalitat a nivell mundial, causant entorn a 18 milions de morts cada any. De elles, la més comuna és la malaltia isquèmica cardíaca, habitualment denominada infart de miocardi (IM). Després de superar un IM, un considerable nombre de pacients desenvolupen taquicàrdies ventriculars (TV) potencialment mortals durant la fase crònica de l'IM, és a dir, setmanes, mesos i fins i tot anys després de la fase aguda inicial. Aquest tipus concret de TV normalment s'origina per una reentrada a través dels canals de conducció (CC), filaments de miocardi supervivent que travessen la cicatriu de l'infart fibrosa i no conductora. Quan els fàrmacs anti-arítmics resulten incapaços d'evitar episodis recurrents de TV, l'ablació per radiofreqüència (ARF), un procediment mínimament invasiu realitzat mitjançant cateterisme en el laboratori de electrofisiologia (EF), s'usa habitualment per a interrompre de manera permanent la propagació elèctrica a través dels CCs responsables de la TV. No obstant això, a més de ser invasiu, arriscat i requerir molt de temps, en casos de TVs relacionades amb IM crònic fins a un 50% dels pacients continua patint episodis recurrents de TV després del procediment d'ARF. Per tant, existeix la necessitat de desenvolupar noves estratègies pre-procediment per a millorar la planificació de l'ARF i, d'aquesta manera, augmentar la taxa d'èxit, que es relativament baixa. En primer lloc, realitzem una revisió exhaustiva de la literatura referent als models cardíacs 3D existents, amb la finalitat d'obtindre un profund coneixement de les seues principals característiques i els mètodes usats en la seua construcció, amb especial atenció sobre els models orientats a simulació de EF cardíaca. Posteriorment, usant dades clíniques d'un pacient amb historial de TV relacionada amb infart, dissenyem i implementem una sèrie d'estratègies i metodologies per a (1) generar models computacionals 3D específics de pacient de ventricles infartats capaços de realitzar simulacions de EF cardíaca a nivell d'òrgan, incloent la cicatriu de l'infart i la regió circumdant coneguda com a zona de vora (ZV); (2) construir models 3D de tors que permeten l'obtenció del ECG simulat; i (3) dur a terme estudis in-silico de EF personalitzats i pre-procediment, tractant de replicar els vertaders estudis de EF realitzats en el laboratori de EF abans de l'ablació. La finalitat d'aquestes metodologies és la de localitzar els CCs en el model ventricular 3D per a ajudar a definir els objectius d'ablació òptims per al procediment d'ARF. Finalment, a manera de prova de concepte, realitzem l'estudi retrospectiu per simulació d'un cas, en el qual aconseguim induir la TV reentrant relacionada amb l'infart usant diferents configuracions de modelatge per a la ZV. Validem els nostres resultats mitjançant la reproducció, amb una precisió raonable, del ECG del pacient en TV, així com en ritme sinusal a partir dels mapes d'activació endocardíac obtinguts invasivament mitjançant sistemes de mapatge electro-anatòmic en aquest últim cas. Això va permetre trobar la ubicació i analitzar les característiques del CC responsable de la TV clínica. Cal destacar que aquest estudi in-silico de EF podria haver-se efectuat abans del procediment d'ARF, ja que el nostre plantejament està completament basat en dades clíniques no invasius adquirits abans de la intervenció real. Aquests resultats confirmen la viabilitat de la realització d'estudis in-silico de EF personalitzats i pre-procediment d'utilitat, així com el potencial de l'abordatge proposat per a arribar a ser en un futur una eina de suport per a la planificació de l'ARF en casos de TVs reentrants relacionades amb infart. No obstant això, la metodologia proposada requereix de notables millores i validació per mitjà d'estudis de simulació amb grans cohorts de pacients.[EN] Cardiovascular diseases represent the main cause of morbidity and mortality worldwide, causing around 18 million deaths every year. Among these diseases, the most common one is the ischaemic heart disease, usually referred to as myocardial infarction (MI). After surviving to a MI, a considerable number of patients develop life-threatening ventricular tachycardias (VT) during the chronic stage of the MI, that is, weeks, months or even years after the initial acute phase. This particular type of VT is typically sustained by reentry through slow conducting channels (CC), which are filaments of surviving myocardium that cross the non-conducting fibrotic infarct scar. When anti-arrhythmic drugs are unable to prevent recurrent VT episodes, radiofrequency ablation (RFA), a minimally invasive procedure performed by catheterization in the electrophysiology (EP) laboratory, is commonly used to interrupt the electrical conduction through the CCs responsible for the VT permanently. However, besides being invasive, risky and time-consuming, in the cases of VTs related to chronic MI, up to 50% of patients continue suffering from recurrent VT episodes after the RFA procedure. Therefore, there exists a need to develop novel pre-procedural strategies to improve RFA planning and, thereby, increase this relatively low success rate. First, we conducted an exhaustive review of the literature associated with the existing 3D cardiac models in order to gain a deep knowledge about their main features and the methods used for their construction, with special focus on those models oriented to simulation of cardiac EP. Later, using a clinical dataset of a chronically infarcted patient with a history of infarct-related VT, we designed and implemented a number of strategies and methodologies to (1) build patient-specific 3D computational models of infarcted ventricles that can be used to perform simulations of cardiac EP at the organ level, including the infarct scar and the surrounding region known as border zone (BZ); (2) construct 3D torso models that enable to compute the simulated ECG; and (3) carry out pre-procedural personalized in-silico EP studies, trying to replicate the actual EP studies conducted in the EP laboratory prior to the ablation. The goal of these methodologies is to allow locating the CCs into the 3D ventricular model in order to help in defining the optimal ablation targets for the RFA procedure. Lastly, as a proof-of-concept, we performed a retrospective simulation case study, in which we were able to induce an infarct-related reentrant VT using different modelling configurations for the BZ. We validated our results by reproducing with a reasonable accuracy the patient's ECG during VT, as well as in sinus rhythm from the endocardial activation maps invasively recorded via electroanatomical mapping systems in this latter case. This allowed us to find the location and analyse the features of the CC responsible for the clinical VT. Importantly, such in-silico EP study might have been conducted prior to the RFA procedure, since our approach is completely based on non-invasive clinical data acquired before the real intervention. These results confirm the feasibility of performing useful pre-procedural personalized in-silico EP studies, as well as the potential of the proposed approach to become a helpful tool for RFA planning in cases of infarct-related reentrant VTs in the future. Nevertheless, the developed methodology requires further improvements and validation by means of simulation studies including large cohorts of patients.During the carrying out of this doctoral thesis, the author Alejandro Daniel López Pérez was financially supported by the Ministerio de Economía, Industria y Competitividad of Spain through the program Ayudas para contratos predoctorales para la formación de doctores, with the grant number BES-2013-064089.López Pérez, AD. (2019). Computational modelling of the human heart and multiscale simulation of its electrophysiological activity aimed at the treatment of cardiac arrhythmias related to ischaemia and Infarction [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/124973TESI

Three-dimensional Multiscale Modelling and Simulation of Atria and Torso Electrophysiology

A better understanding of the electrical activity of the heart under physiological and pathological conditions has always been key for clinicians and researchers. Over the last years, the information in the P-wave signals has been extensively analysed to un-cover the mechanisms underlying atrial arrhythmias by localizing ectopic foci or high-frequency rotors. However, the relationship between the activation of the different areas of the atria and the characteristics of the P-wave signals or body surface poten-tial maps are still far from being completely understood. Multiscale anatomical and functional models of the heart are a new technological framework that can enable the investigation of the heart as a complex system. This thesis is centred in the construction of a multiscale framework that allows the realistic simulation of atrial and torso electrophysiology and integrates all the anatom-ical and functional descriptions described in the literature. The construction of such model involves the development of heterogeneous cellular and tissue electrophysiolo-gy models fitted to empirical data. It also requires an accurate 3D representation of the atrial anatomy, including tissue fibre arrangement, and preferential conduction axes. This multiscale model aims to reproduce faithfully the activation of the atria under physiological and pathological conditions. We use the model for two main applica-tions. First, to study the relationship between atrial activation and surface signals in sinus rhythm. This study should reveal the best places for recording P-waves signals in the torso, and which are the regions of the atria that make the most significant contri-bution to the body surface potential maps and determine the main P-wave characteris-tics. Second, to spatially cluster and classify ectopic atrial foci into clearly differenti-ated atrial regions by using the body surface P-wave integral map (BSPiM) as a bi-omarker. We develop a machine-learning pipeline trained from simulations obtained from the atria-torso model aiming to validate whether ectopic foci with similar BSPiM naturally cluster into differentiated non-intersected atrial regions, and whether new BSPiM could be correctly classified with high accuracy.En la actualidad, una mejor compresión de la actividad eléctrica del corazón en condi-ciones fisiológicas y patológicas es clave para médicos e investigadores. A lo largo de los últimos años, la información derivada de la onda P se ha utilizado para intentar descubrir los mecanismos subyacentes a las arritmias auriculares mediante la localiza-ción de focos ectópicos y rotores de alta frecuencia. Sin embargo, la relación entre la activación de distintas regiones auriculares y las características tanto de las ondas P como de la distribución de potencial en la superficie del torso está lejos de entenderse completamente. Los modelos cardíacos funcionales y anatómicos son una nueva he-rramienta que puede facilitar la investigación relativa al corazón entendido como sis-tema complejo. La presente tesis se centra en la construcción de un modelo multiescala para la simula-ción realista de la electrofisiología cardíaca tanto a nivel auricular como de torso, integrando toda la información anatómica y funcional disponible en la literatura. La construcción de este modelo implica el desarrollo, en base a datos experimentales, de modelos electrofisiológicos heterogéneos tanto celulares como tisulares. Así mismo, es imprescindible una representación tridimensional precisa de la anatomía auricular, incluyendo la dirección de fibras y los haces de conducción preferentes. Este modelo multiescala busca reproducir fielmente la activación auricular en condiciones fisiológi-cas y patológicas. Su uso se ha centrado fundamentalmente en dos aplicaciones. En primer lugar, estudiar la relación entre la activación auricular en ritmo sinusal y las señales en la superficie del torso. Este estudio busca definir la mejor ubicación para el registro de las ondas P en el torso así como determinar aquellas regiones auriculares que contribuyen fundamentalmente a la formación y distribución de potenciales super-ficiales así como a las características de las ondas P. En segundo lugar, agrupar y cla-sificar espacialmente los focos ectópicos en regiones auriculares claramente diferen-ciables empleando como biomarcador los mapas superficiales de integral de la onda P (BSPiM). Se ha desarrollado para ello una metodología de aprendizaje automático en la que las simulaciones obtenidas con el modelo multiescala aurícula-torso sirven de entrenamiento, permitiendo validar si los focos ectópicos cuyos BSPiMs son similares se agrupan de forma natural en regiones auriculares no intersectadas y si BSPiMs nue-vos podrían ser clasificados prospectivamente con gran precisión.Avui en dia, una millor comprenssió de l'activitat elèctrica del cor en condicions fisio-lògiques i patològiques és clau per a metges i investigadors. Al llarg dels últims anys, la informació derivada de l'ona P s'ha utilitzat per intentar descobrir els mecanismes subjacents a les arítmies auriculars mitjançant la localització de focus ectòpics i rotors d'alta freqüència. No obstant això, la relació entre l'activació de diferents regions auri-culars i les característiques tant de les ones P com de la distribució de potencial en la superfície del tors està lluny d'entendre's completament. Els models cardíacs funcionals i anatòmics són una nova eina que pot facilitar la recerca relativa al cor entès com a sistema complex. La present tesi es centra en la construcció d'un model multiescala per a la simulació realista de la electrofisiologia cardíaca tant a nivell auricular com de tors, integrant tota la informació anatòmica i funcional disponible en la literatura. La construcció d'aquest model implica el desenvolupament, sobre la base de dades experimentals, de models electrofisiològics heterogenis, tant cel·lulars com tissulars. Així mateix, és imprescindible una representació tridimensional precisa de l'anatomia auricular, in-cloent la direcció de fibres i els feixos de conducció preferents. Aquest model multies-cala busca reproduir fidelment l'activació auricular en condicions fisiològiques i pa-tològiques. El seu ús s'ha centrat fonamentalment en dues aplicacions. En primer lloc, estudiar la relació entre l'activació auricular en ritme sinusal i els senyals en la superfí-cie del tors. A més a més, amb aquest estudi també es busca definir la millor ubicació per al registre de les ones P en el tors, així com, determinar aquelles regions auriculars que contribueixen fonamentalment a la formació i distribució de potencials superfi-cials a l'hora que es caracteritzen les ones P. En segon lloc, agrupar i classificar espa-cialment els focus ectòpics en regions auriculars clarament diferenciables emprant com a biomarcador els mapes superficials d'integral de l'ona P (BSPiM). És per això que s'ha desenvolupat una metodologia d'aprenentatge automàtic en la qual les simulacions obtingudes amb el model multiescala aurícula-tors serveixen d'entrenament, la qual cosa permet validar si els focus ectòpics, llurs BSPiMs són similars, s'agrupen de for-ma natural en regions auriculars no intersectades i si BSPiMs nous podrien ser classifi-cats de manera prospectiva amb precisió.Ferrer Albero, A. (2017). Three-dimensional Multiscale Modelling and Simulation of Atria and Torso Electrophysiology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88402TESI

Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

Patienten mit Vorhofflimmern sind einem fünffach erhöhten Risiko für einen ischämischen Schlaganfall ausgesetzt. Eine frühzeitige Erkennung und Diagnose der Arrhythmie würde ein rechtzeitiges Eingreifen ermöglichen, um möglicherweise auftretende Begleiterkrankungen zu verhindern. Eine Vergrößerung des linken Vorhofs sowie fibrotisches Vorhofgewebe sind Risikomarker für Vorhofflimmern, da sie die notwendigen Voraussetzungen für die Aufrechterhaltung der chaotischen elektrischen Depolarisation im Vorhof erfüllen. Mithilfe von Techniken des maschinellen Lernens könnten Fibrose und eine Vergrößerung des linken Vorhofs basierend auf P Wellen des 12-Kanal Elektrokardiogramms im Sinusrhythmus automatisiert identifiziert werden. Dies könnte die Basis für eine nicht-invasive Risikostrat- ifizierung neu auftretender Vorhofflimmerepisoden bilden, um anfällige Patienten für ein präventives Screening auszuwählen. Zu diesem Zweck wurde untersucht, ob simulierte Vorhof-Elektrokardiogrammdaten, die dem klinischen Trainingssatz eines maschinellen Lernmodells hinzugefügt wurden, zu einer verbesserten Klassifizierung der oben genannten Krankheiten bei klinischen Daten beitra- gen könnten. Zwei virtuelle Kohorten, die durch anatomische und funktionelle Variabilität gekennzeichnet sind, wurden generiert und dienten als Grundlage für die Simulation großer P Wellen-Datensätze mit genau bestimmbaren Annotationen der zugrunde liegenden Patholo- gie. Auf diese Weise erfüllen die simulierten Daten die notwendigen Voraussetzungen für die Entwicklung eines Algorithmus für maschinelles Lernen, was sie von klinischen Daten unterscheidet, die normalerweise nicht in großer Zahl und in gleichmäßig verteilten Klassen vorliegen und deren Annotationen möglicherweise durch unzureichende Expertenannotierung beeinträchtigt sind. Für die Schätzung des Volumenanteils von linksatrialem fibrotischen Gewebe wurde ein merkmalsbasiertes neuronales Netz entwickelt. Im Vergleich zum Training des Modells mit nur klinischen Daten, führte das Training mit einem hybriden Datensatz zu einer Reduzierung des Fehlers von durchschnittlich 17,5 % fibrotischem Volumen auf 16,5 %, ausgewertet auf einem rein klinischen Testsatz. Ein Long Short-Term Memory Netzwerk, das für die Unterscheidung zwischen gesunden und P Wellen von vergrößerten linken Vorhöfen entwickelt wurde, lieferte eine Genauigkeit von 0,95 wenn es auf einem hybriden Datensatz trainiert wurde, von 0,91 wenn es nur auf klinischen Daten trainiert wurde, die alle mit 100 % Sicherheit annotiert wurden, und von 0,83 wenn es auf einem klinischen Datensatz trainiert wurde, der alle Signale unabhängig von der Sicherheit der Expertenannotation enthielt. In Anbetracht der Ergebnisse dieser Arbeit können Elektrokardiogrammdaten, die aus elektrophysiologischer Modellierung und Simulationen an virtuellen Patientenkohorten resul- tieren und relevante Variabilitätsaspekte abdecken, die mit realen Beobachtungen übereinstim- men, eine wertvolle Datenquelle zur Verbesserung der automatisierten Risikostratifizierung von Vorhofflimmern sein. Auf diese Weise kann den Nachteilen klinischer Datensätze für die Entwicklung von Modellen des maschinellen Lernens entgegengewirkt werden. Dies trägt letztendlich zu einer frühzeitigen Erkennung der Arrhythmie bei, was eine rechtzeitige Auswahl geeigneter Behandlungsstrategien ermöglicht und somit das Schlaganfallrisiko der betroffenen Patienten verringert