Larval zebrafish electrocardiography electrodynmaic modelling and sensor design

This thesis presents the first model of the electrical activity of the larval zebrafish heart as well as the design and fabrication of novel electrode arrays that were created to measure the electrocardiogram. The model consists of realistic 3D geometry of a 3 day’s post fertilisation zebrafish heart and body with a bidomain electrical model that uses the Fitzhugh-Nagumo equations as the ionic model. The model is able to replicate experimentally observed conduction velocities and action potentials by using region specific parameters and simulate electrocardiograms that are comparable to measurements. The electrode arrays are constructed from flexible polyimide films with gold microelectrodes. These devices have the potential to improve the measurement of the electrocardiogram for drug screening applications as an alternative to the use of micropipette electrodes. Gold plating and PEDOT:PSS coating techniques were applied to the devices to successfully reduce electrode impedance with the effectiveness of each technique categorised using impedance spectroscopy. The devices were tested $in$ $vivo$ with larval zebrafish with limited success and so $in$ $vitro$ tests were conducted using an artificial current source

Catheter ablation in patients with atrial fibrillation : mapping refinements, outcome prediction and effect on quality of life

PhD ThesisChapter 1 presents a literature review, focused primarily on the pathophysiology and management of atrial fibrillation (AF). Chapter 2 examines correlations between the dominant frequency of AF - calculated using principal component analysis from a modified surface 12-lead ECG (which included posterior leads), a standard 12-lead ECG and intracardiac recordings from both atria. The inclusion of posterior leads did not improve correlation with left atrial activity because of the dominance of lead V1 in both ECG configurations. Chapter 3 explores whether acute and 12-month outcome following catheter ablation for AF can be predicted beforehand from clinical and surface AF waveform parameters. Multivariate risk scores combining these parameters can predict arrhythmia outcome following ablation, and could therefore be used to identify those most likely to benefit from this therapy. Chapter 4 examines the effect of catheter ablation on AF symptoms and quality of life (QoL). AF symptom and QoL scores improved significantly in patients who maintained sinus rhythm after ablation but did not change in those with recurrent AF. AF-specific QoL scales are more responsive to change and correlate better with ablation outcome. Chapter 5 examines inter-atrial frequency gradients in patients with persistent AF using multipolar contact mapping. A right-to-left atrial frequency gradient was found in a quarter of the patients studied, implying that their arrhythmia was being maintained by high frequency sources in the right rather than the left atrium. Chapter 6 examines whether targeting high frequency and highly repetitive complex fractionated atrial electrogram sites, identified using multipolar contact mapping during persistent AF, resulted in arrhythmia termination and maintenance of sinus rhythm long-term. The utility of administering flecainide to distinguish critical from bystander AF sites was also investigated. Flecainide did not help refine ablation targets and 12-month outcome after targeting these sites was not superior to other ablation strategies

Spatial Characterization and Estimation of Intracardiac Propagation Patterns During Atrial Fibrillation

This doctoral thesis is in the field of biomedical signal processing with focus on methods for the analysis of atrial fibrillation (AF). Paper I of the present thesis addresses the challenge of extracting spatial properties of AF from body surface signals. Different parameters are extracted to estimate the preferred direction of atrial activation and the complexity of the atrial activation pattern. In addition, the relation of the spatial properties to AF organization, which is quantified by AF frequency, is evaluated. While no significant correlation between the preferred direction of atrial activation and AF frequency could be observed, the complexity of the atrial activation pattern was found to increase with AF frequency. The remaining three papers deal with the analysis of the propagation of the electrical activity in the atria during AF based on intracardiac signals. In Paper II, a time-domain method to quantify propagation patterns along a linear catheter based on the detected atrial activation times is developed. Taking aspects on intra-atrial signal organization into account, the detected activation times are combined into wavefronts, and parameters related to the consistency of the wavefronts over time and the activation order along the catheter are extracted. Furthermore, the potential relationship of the extracted parameters to established measures from body surface signals is investigated. While the degree of wavefront consistency was not reflected by the applied body surface measures, AF frequency could distinguish between recordings with different degrees of intra-atrial signal organization. This supports the role of AF frequency as an organization measure of AF. In Paper III, a novel method to analyze intracardiac propagation patterns based on causality analysis in the frequency domain is introduced. In particular, the approach is based on the partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The potential of the method is illustrated with simulation scenarios based on a detailed ionic model of the human atrial cell as well as with real data recordings, selected to present typical propagation mechanisms and recording situations in atrial tachyarrhythmias. For simulated data, the PDC is correctly reflecting the direction of coupling and thus the propagation between all recording sites. For real data, clear propagation patterns are identified which agree with previous clinical observations. Thus, the results illustrate the ability of the novel approach to identify propagation patterns from intracardiac signals during AF which can provide important information about the underlying AF mechanisms, potentially improving the planning and outcome of ablation. However, spurious couplings over long distances can be observed when analyzing real data comprised by a large number of simultaneously recorded signals, which gives room for further improvement of the method. The derivation of the PDC is entirely based on the fit of a multivariate autoregressive (MVAR) model, commonly estimated by the least-squares (LS) method. In Paper IV, the adaptive group least absolute selection and shrinkage operator (LASSO) is introduced in order to avoid overfitting of the MVAR model and to incorporate prior information such as sparsity of the solution. The sparsity can be motivated by the observation that direct couplings over longer distances are likely to be zero during AF; an information which has been further incorporated by proposing distance-adaptive group LASSO. In simulations, adaptive and distance-adaptive group LASSO are found to be superior to LS estimation in terms of both detection and estimation accuracy. In addition, the results of both simulations and real data analysis indicate that further improvements can be achieved when the distance between the recording sites is known or can be estimated. This further promotes the PDC as a method for analysis of AF propagation patterns, which may contribute to a better understanding of AF mechanisms as well as improved AF treatment

Contributions to electrocardiographic science

This thesis reports original theoretical and experimental studies related to the measurement and interpretation of the electrical activity of the heart. The relevant literature and clinical practice are reviewed at length. Part I is a review of the science of electrocardiography. Included in the review are the electrophysiology of the heart, the potential theory which relates the electrocardiogram (E.C.G.) to its source, the various schemes used to measure and interpret the E.C.G. and the use of computer modelling to aid in E.C.G. interpretation. The effects of body shape and internal conductivity inhomogeneities on the E.C.G. are studied by means of a computer model. A simple form of the model has a piecewise homogeneous interior with spherical boundaries and a surface admittance is invoked to model changes in the surface shape. An extended form of the model allows the boundaries to be irregular and it is solved by means of an integral equation and the extended boundary condition. Representative numerical results are presented, illustrating the practical utility of the model. The sensitivity of the E.C.G. to certain types of inhomogeneity and surface shape changes is established. An experimental study, supported by a computer model based on the techniques outlined above, of the non-invasive detection of the signals from the ventricular specialised conduction system is reported. Thirty-five subjects were studied using a measurement system with a frequency response extending from 0.1 Hz to 500 Hz (-3 dB points) and using a pair of chest electrodes (similar to Lead CM1), Signal averaging was performed on groups of approximately 50 beats, using the onset of the QRS wave as a timing reference. The signals were detected with certainty in 85% of the subjects studied. The typical measured signal waveform is remarkably similar to that predicted by the aforementioned computer modelling technique. Two features are identified: an initial positive deflection (which probably represents the initial activation of the bundle branches) and a notch approximately 10 msec later (which may represent the passage of the activation into the bundle branches)

Development of a medium-high throughput electrophysiology method to study cellular heterogeneity in the rabbit heart

Sudden cardiac death (SCD) is a prominent cause of death worldwide today, mainly occurring as a result of coronary heart disease, cardiomyopathies, and inherited or induced arrhythmia syndromes. Survival following sudden cardiac arrest (SCA) has improved in the past decades, but the majority of cases of SCD remain unwitnessed. Although advances have been made towards the investigation of the mechanisms behind SCD, it remains a poorly understood phenomenon. Environmental factors have been identified and associated with increased arrhythmic risk, and most prominently, drug-induced arrhythmias constitute a serious hurdle to both cardiac and non-cardiac drug development. The past decade has seen pro-arrhythmic screening of new compounds become routine, and develop into a major point of interest for drug development. Specifically, the onset of drug-induced polymorphic ventricular tachycardia, such as torsade de pointes (TdP), is of particular interest to cardiac research. The concept of electrophysiological heterogeneity in cardiac muscle holds exciting potential for explaining the pathophysiology of TdP, but quantifying cellular heterogeneity using conventional methods is a challenge. This work developed and refined a fluorescence-based, medium/high-throughput electrophysiological assay to process large cell populations (~50-500 cells) from single hearts. Using this novel approach, transmural electrophysiological differences were found between regions of individual hearts, replicating published work with a 3 to 4-fold reduction in hearts sampled, and additionally providing a previously unknown quantification of cellular heterogeneity in isolated cardiomyocyte populations, in both healthy and failing rabbit hearts. Further classification of electrophysiological differences within smaller regions of the ventricle yielded evidence of repolarisation gradients across the myocardium, with vast overlap in repolarisation duration, challenging the dogma of region-specific repolarisation duration. Lastly, by specifically blocking hERG channels and L-type calcium channels in cardiac subregions (sub-epicardial apex and base) strong evidence was found for heterogeneous electrophysiology response amongst isolated cell populations. Specifically, sub-epicardial action potential shortening using nifedipine was strongly APD dependent, whereby baseline AP duration determined the extent of APD shortening via drug-induced ICa-L blockade. Sub-epicardial AP prolongation mediated via IKr block using dofetilide also produced non-homogeneous cell response in the form of two distinct population responses: (i) The majority (~85%) was made up of normal responding cells, experiencing ~20-30ms AP prolongation not dependent on baseline APD (P100ms AP prolongation, beyond the pacing cycle length (>500ms) without any evidence of early-afterdepolarisations. Large experimental samples of AP parameters gathered in this study can provide real-world data parameter space ranges for mathematical model development, showing that ion channel conductance ranges used today to predict drug responses at the organ level may be too restrictive, or inaccurate. Iterative model adjustment using large experimental datasets can help constrain models and improve their predictive power, saving time by reducing computational power required

Bioelectric Signal Analysis to Expose Nervous Control of the Human Heart

This thesis describes the development of new methods to infer the nature of nervous control of the human heart using recordings of its electrical behaviour. Malfunctions of this control system are a leading cause of death, and can be triggered by a diverse range of influences including basic physiological factors and one’s emotional state. However, the mechanisms of failure remain poorly understood, partly due to a lack of relevant human data. The principal purpose of the work described in this thesis is to improve the availability of such data. A literature review was conducted, covering the current understanding of electrical activity in the heart and its control by the nervous system, as well as the techniques available to observe that behaviour. A variety of novel techniques were developed and implemented experimentally to demonstrate their utility. Specialised methods for the filtering and subsequent spectral analysis of electrocardiograph (ECG) signals were used to expose differences between psychologically distinct groups in terms of their response to emotional stimuli. Algorithms were developed to automatically process unipolar electrogram recordings with minimal human intervention, enabling the analysis of heterogeneous electrophysiological dynamics, which requires datasets of a size that would otherwise make in-depth analyses intractable. New indices were developed for measuring the timing of localised electrical activation and recovery from unipolar electrograms, in order to overcome the fact that conventional indices are not well suited to dynamic analyses. Experiments using these tools demonstrated that respiration induces heart-rate independent modulation of the ventricles’ electrophysiological behaviour via the autonomic nervous system. By improving the accessibility of human in situ data, the developed tools enable new research methodologies to study interactions between the heart and the nervous system, which may ultimately contribute to the development of new treatments to prevent thousands of deaths in the UK alone each year

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature