11 research outputs found
Simplified Cardiodynamic Tissue Electrophysiology Characterization, Reduced Order Modeling with Therapeutic Perspective
Atrial fibrillation (Afib) is the most common cardiac arrhythmia affecting millions of people around the world. Mapping and analysis of electrical activation patterns such as electric rotors during Afib is crucial in understanding arrhythmic mechanisms and assessment of diagnostic measures. To this end, there exists various mapping studies where textit{'quantitative'} features such as local activation time, dominant frequency, wave direction, and conduction velocity are extracted from recorded intracardiac electrograms (EGMs). However, obtaining quantitative features further adds to multiplicity of the data and henceforth does not help interpretation of measured signals as opposed to using a more compressed diagnostic terms such as linking the measurements to reentry mechanisms. Through some techniques it is possible to construct isopotential and phase mappings by the help of monophasic action potential recordings in higher spatial resolution. In those cases, however, both expensive mapping tools performing multi-site simultaneous recordings which are not available to most of electrophysiologists are required. On the other hand, the most commonly used catheters which provide high resolution but local measurements remain rather rudimentary in mapping a spatially more global arrhythmic behaviors in a simultaneous fashion. Spiral waves are tissue level phenomena observed in both clinical and experimental settings. They are the product of electrical rotors which are associated with reentry mechanisms during Afib. They can be reproduced using computer models of cardiac electrical activity. Current computer models vary in complexity, accuracy, and efficiency. One particular type is called biophysical models which are based on detailed ion channel interactions. Besides being computationally demanding, they are exceedingly complex and intractable preventing their use in a systems approach where multilevel events are generally considered together. Phenomenological models, on the other hand, include summarized details of ionic events yet preserve fundamental biophysical accuracy. A particular one of them, a minimal resistor model (MRM), was shown to reproduce relevant basic electrophysiological behaviors such as (action potential) AP and electrical restitution properties for human ventricular tissue. The objective in present thesis is to 'qualitatively' characterize fibrillatory wavefront propagation dynamics in cardiac tissue using simulated intracardiac EGMs obtained from most commonly used and lower cost catheter types providing high resolution but localized readings. Another purpose connected to the previous is to show adequacy of a phenomenological model, MRM, in reproducing biophysically related behaviors for human atria. In this respect, two category of problems are handled throughout the thesis: (1) parameter estimation of MRM and (2) discrimination of spiral wave behaviors through intracardiac EGMs simulated using MRM. In the first part, representativeness of MRM for human atrial electrophysiology is established through adaptation of it to a biophysically detailed model originated from experimental data. Specifically, a method is proposed for parameter estimation of the simple model, MRM, to match a targeted behavior such as AP and electrical restitutions first generated from a complex model, by using extended Kalman filter (EKF). In the second part, a method that receives intracardiac EGMs and returns corresponding wavefront propagation patterns classified in terms of electric rotor dynamics is introduced. The method incorporates an information theoretical distance which is called normalized compression distance (NCD) used for assessment of distance measure between simulated behaviors. Achieving outstanding performance together with robustness in discrimination through usage of simulated data enables a theoretical validation of the method. Proposed frameworks collectively yield (1) potential usability of a computationally efficient and easier in analysis model for tissue level cardiac events and (2) simplicity and practicality in clinics through a mapping from a multiple, complex EGM signals to electric rotor behaviors, symptoms more relevant to the diagnosis.Ph.D., Electrical Engineering -- Drexel University, 201
Signal Processing Methods for the Analysis of the Electrocardiogram
Das Elektrokardiogramm (EKG) zeichnet die elektrische AktivitĂ€t des Herzens auf der Brust- oberflĂ€che auf. Dieses Signal kann einfach und kostengĂŒnstig aufgenommen werden und wird daher in einer Vielzahl von mobilen und stationĂ€ren Anwendungen genutzt. Es ist ĂŒber die letzten 100 Jahre zum Goldstandard bei der Diagnose vieler kardiologischer Krankheiten geworden. Herzerkrankungen bleiben ein relevantes Thema in unserer Gesellschaft, da sie zu 30 % aller TodesfĂ€lle weltweit fĂŒhren. Allein die koronare Herzkrankheit ist die hĂ€ufigste Todesursache ĂŒberhaupt. Weiterhin sind 2 bis 3 % der EuropĂ€er von Herzrhythmusstörungen wie Vorhofflimmern und Vorhofflattern betroffen. Die damit verbundenen geschĂ€tzten Kosten in der EuropĂ€ischen Union belaufen sich auf 26 Milliarden Euro pro Jahr. In allen diesen FĂ€llen ist die Aufzeichnung des EKGs der erste unumgĂ€ngliche Schritt fĂŒr eine verlĂ€ssliche Diagnose und erfolgreiche Therapie.
Im Rahmen dieser Dissertation wurden eine Reihe von Algorithmen zur Signalver- arbeitung des EKG entwickelt, die automatisch die rhythmischen und morphologischen Eigenschaften aus dem EKG extrahieren und dadurch den diagnostischen Prozess und die Entscheidungsfindung des Arztes unterstĂŒtzen. In einem ersten Projekt wurde das PhĂ€nomen der postextrasystolischen T-Wellen-Ănderung (PEST) untersucht. Die aus der PEST ex- trahierten Biomarker haben wir als PrĂ€diktoren fĂŒr Herzversagen postuliert. Ein zweites Projekt handelte vom Entwurf eines akkuraten Algorithmus zur Detektion und Annotation der P-Welle im EKG. Als Referenz wĂ€hrend der Entwicklung wurden intrakardial gemessene Signale verwendet. Eine dritte Untersuchg hatte das Ziel, das physiologische PhĂ€nomen der respiratorischen Sinusarrhythmie (RSA) besser zu verstehen. In diesem Projekt wurde ein Algorithmus zur Trennung der HerzratenvariabilitĂ€t (HRV) in ihre atmungsabhĂ€ngige und ihre atmungsunabhn Ìgige Komponente untersucht. Letzterer Anteil der HRV könnte neue Erkenntnisse ĂŒber die Regulationsmechanismen des kardiovaskulĂ€ren Systems liefern. In der vierten und letzten Studie wurde der Einfluss mentaler Belastung auf das EKG wĂ€hrend der Autofahrt untersucht. Eine Vielzahl von Deskriptoren wurden gefunden, die eine gefĂ€hrliche mentale Beanspruchung detektieren und somit den Fahrer vor einem möglichen Unfall schĂŒtzen können.
Wir schlieĂen aus diesen Untersuchungen, dass gut entwickelte Methoden der Signalver- arbeitung des EKG das Potential haben, die Belastung der Patienten, die an Herzerkrankungen leiden, und die Anzahl der VerkehrsunfĂ€lle zu reduzieren
Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation
Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning
(ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms
have struggled to keep up, despite their superior capabilities. This is mainly attributed
to the need for large amounts of data for training, which the scientific community is unable to
satisfy.
The number of promising DL algorithms is considerable, although solutions directly targeting
the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on
single, classical modalities and tend to complicate significantly with the amount of physiological
effects they can simulate.
This thesis aims at providing and validating a framework, specifically addressing the data
deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was
designed to generate large, annotated artificial signals. By expressing data through coefficients of
pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and
intra-modality associations were learned. Thereafter, new coefficients are sampled to generate
artificial, multimodal signals with the original physiological dynamics. Moreover, normal and
pathological beats along with artifacts were included by employing Markov models. Secondly,
a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture
and trained with synthesized data under real-world experimental conditions to evaluate how its
performance is affected.
Both the synthesizer and the CNN not only performed at state of the art level but also innovated
with multiple types of generated data and detection error improvements, respectively.
Cardiorespiratory data augmentation corrected performance drops when not enough data is available,
enhanced the CNNâs ability to perform on noisy signals and to carry out new tasks when
introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully
validated showing potential to leverage future DL research on Cardiology into clinical standards
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Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach
INTRODUCTION
Heart function can be impaired by rhythm disturbances (cardiac arrhythmia), illustrated by electrocardiogram (ECG) recordings. Computerised arrhythmia diagnosis is well established for ECGâs but less for intracardiac electrophysiological (EP) testing. Accurate diagnosis is pre-requisite for delivering appropriate treatment to patients however existing algorithms misdiagnose a proportion of arrhythmias. Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies with potential application as a generic rhythm classifier.
METHOD
An ethically approved prospective clinical study collected clinical history, right atrial and right ventricular intracardiac electrograms, beat-to-beat cardiac stroke volume, body motion and body temperature data during EP studies. An iterative system development life-cycle was used, including knowledge management and classifier development sub-processes. Domain expert knowledge and clinical arrhythmia diagnosis were modelled, synthesised as AI classifiers and used to classify cardiac rhythms.
RESULTS
Data collected from 65 patients was pre-processed into instances for classifier inputs. Decision tree, naĂŻve Bayes, neural network, support vector machine and inference engine classifiers developed using Matlab showed good performance and were combined as a production system in a mixture-of-experts multi-classifier system. 18 different rhythms were classified, with the naĂŻve Bayes classifier used to classify 11 rhythms, decision tree 4 rhythms, neural network and support vector machine one each, unclassified instances by the inference engine classifier and final class allocation using decision rule. Production system showed overall correct clasification rate 0.960; error 0.040; mean sensitivity 0.855; mean specificity 0.977; mean Îș 0.767; mean positive predictive value 0.792; mean negative predictive value 0.975; mean Pearsonâs phi 0.787, with P 0.9 for sinus node dysfunction and atrio-ventricular nodal/ junctional tachycardias. Temperature, accelerometry and QT interval were assessed as features by a comparison of algorithm performances with each feature removed and found not to affect classification performance. An evaluation showed 10 beat analysis performed better than 5 beat analysis.
CONCLUSIONS
Modelling of the clinical diagnosis process produced an AI based mixture-of-experts multi-classifier system, which accurately diagnosed different 18 cardiac rhythms. The naĂŻve Bayes classifier performed best and classified 11 rhythms. Features for clinical symptoms and predisposing factors, atrial electrogram morphology and changes in stroke volume were found to influence rhythm classification. High performances encourage further development and potential future improvements include: a larger sample dataset; inclusion of His and coronary sinus electrograms; data mining for unknown features with significant influence on diagnosis; binary classification. The aim to classify rhythm using artificial intelligence suitable for use during EP studies was satisfied and the research hypothesis that it outperformed current algorithms was accepted. The system was likely to be able to accept updates but needs conversion as a precursor to use in a live clinical environment
Planification de lâablation radiofrĂ©quence des arythmies cardiaques en combinant modĂ©lisation et apprentissage automatique
Cardiac arrhythmias are heart rhythm disruptions which can lead to sudden cardiac death. They require a deeper understanding for appropriate treatment planning. In this thesis, we integrate personalized structural and functional data into a 3D tetrahedral mesh of the biventricular myocardium. Next, the Mitchell-Schaeffer (MS) simplified biophysical model is used to study the spatial heterogeneity of electrophysiological (EP) tissue properties and their role in arrhythmogenesis. Radiofrequency ablation (RFA) with the elimination of local abnormal ventricular activities (LAVA) has recently arisen as a potentially curative treatment for ventricular tachycardia but the EP studies required to locate LAVA are lengthy and invasive. LAVA are commonly found within the heterogeneous scar, which can be imaged non-invasively with 3D delayed enhanced magnetic resonance imaging (DE-MRI). We evaluate the use of advanced image features in a random forest machine learning framework to identify areas of LAVA-inducing tissue. Furthermore, we detail the datasetâs inherent error sources and their formal integration in the training process. Finally, we construct MRI-based structural patient-specific heart models and couple them with the MS model. We model a recording catheter using a dipole approach and generate distinct normal and LAVA-like electrograms at locations where they have been found in clinics. This enriches our predictions of the locations of LAVA-inducing tissue obtained through image-based learning. Confidence maps can be generated and analyzed prior to RFA to guide the intervention. These contributions have led to promising results and proofs of concepts.Les arythmies sont des perturbations du rythme cardiaque qui peuvent entrainer la mort subite et requiĂšrent une meilleure comprĂ©hension pour planifier leur traitement. Dans cette thĂšse, nous intĂ©grons des donnĂ©es structurelles et fonctionnelles Ă un maillage 3D tĂ©traĂ©drique biventriculaire. Le modĂšle biophysique simplifiĂ© de Mitchell-Schaeffer (MS) est utilisĂ© pour Ă©tudier lâhĂ©tĂ©rogĂ©nĂ©itĂ© des propriĂ©tĂ©s Ă©lectrophysiologiques (EP) du tissu et leur rĂŽle sur lâarythmogĂ©nĂšse. Lâablation par radiofrĂ©quence (ARF) en Ă©liminant les activitĂ©s ventriculaires anormales locales (LAVA) est un traitement potentiellement curatif pour la tachycardie ventriculaire, mais les Ă©tudes EP requises pour localiser les LAVA sont longues et invasives. Les LAVA se trouvent autour de cicatrices hĂ©tĂ©rogĂšnes qui peuvent ĂȘtre imagĂ©es de façon non-invasive par IRM Ă rehaussement tardif. Nous utilisons des caractĂ©ristiques dâimage dans un contexte dâapprentissage automatique avec des forĂȘts alĂ©atoires pour identifier des aires de tissu qui induisent des LAVA. Nous dĂ©taillons les sources dâerreur inhĂ©rentes aux donnĂ©es et leur intĂ©gration dans le processus dâapprentissage. Finalement, nous couplons le modĂšle MS avec des gĂ©omĂ©tries du coeur spĂ©cifiques aux patients et nous modĂ©lisons le cathĂ©ter avec une approche par un dipĂŽle pour gĂ©nĂ©rer des Ă©lectrogrammes normaux et des LAVA aux endroits oĂč ils ont Ă©tĂ© localisĂ©s en clinique. Cela amĂ©liore la prĂ©diction de localisation du tissu induisant des LAVA obtenue par apprentissage sur lâimage. Des cartes de confiance sont gĂ©nĂ©rĂ©es et peuvent ĂȘtre utilisĂ©es avant une ARF pour guider lâintervention. Les contributions de cette thĂšse ont conduit Ă des rĂ©sultats et des preuves de concepts prometteurs