A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Impaired coronary blood flow at higher heart rates during atrial fibrillation: investigation via multiscale modelling

Background. Different mechanisms have been proposed to relate atrial fibrillation (AF) and coronary flow impairment, even in absence of relevant coronary artery disease (CAD). However, the underlying hemodynamics remains unclear. Aim of the present work is to computationally explore whether and to what extent ventricular rate during AF affects the coronary perfusion. Methods. AF is simulated at different ventricular rates (50, 70, 90, 110, 130 bpm) through a 0D-1D multiscale validated model, which combines the left heart-arterial tree together with the coronary circulation. Artificially-built RR stochastic extraction mimics the \emph{in vivo} beating features. All the hemodynamic parameters computed are based on the left anterior descending (LAD) artery and account for the waveform, amplitude and perfusion of the coronary blood flow. Results. Alterations of the coronary hemodynamics are found to be associated either to the heart rate increase, which strongly modifies waveform and amplitude of the LAD flow rate, and to the beat-to-beat variability. The latter is overall amplified in the coronary circulation as HR grows, even though the input RR variability is kept constant at all HRs. Conclusions. Higher ventricular rate during AF exerts an overall coronary blood flow impairment and imbalance of the myocardial oxygen supply-demand ratio. The combined increase of heart rate and higher AF-induced hemodynamic variability lead to a coronary perfusion impairment exceeding 90-110 bpm in AF. Moreover, it is found that coronary perfusion pressure (CPP) is no longer a good measure of the myocardial perfusion for HR higher than 90 bpm.Comment: 8 pages, 5 figures, 3 table

Directed networks as a novel way to describe and analyze cardiac excitation : directed graph mapping

Networks provide a powerful methodology with applications in a variety of biological, technological and social systems such as analysis of brain data, social networks, internet search engine algorithms, etc. To date, directed networks have not yet been applied to characterize the excitation of the human heart. In clinical practice, cardiac excitation is recorded by multiple discrete electrodes. During (normal) sinus rhythm or during cardiac arrhythmias, successive excitation connects neighboring electrodes, resulting in their own unique directed network. This in theory makes it a perfect fit for directed network analysis. In this study, we applied directed networks to the heart in order to describe and characterize cardiac arrhythmias. Proof-of-principle was established using in-silico and clinical data. We demonstrated that tools used in network theory analysis allow determination of the mechanism and location of certain cardiac arrhythmias. We show that the robustness of this approach can potentially exceed the existing state-of-the art methodology used in clinics. Furthermore, implementation of these techniques in daily practice can improve the accuracy and speed of cardiac arrhythmia analysis. It may also provide novel insights in arrhythmias that are still incompletely understood

Detection and measurement and of repolarisation features in atrial fibrillation and healthy subjects

Major cardiac organisations recommended U wave abnormalities should be reported during ECG interpretation. However, U waves cannot be measured in patients with atrial fibrillation (AF) due to the obscuring fibrillatory wave.The first aim of the research was to provide a validated algorithm to clean the ECGs of AF patients by removing the atrial fibrillatory waves so that the characteristics of ventricle repolarisation components, U and T waves, could be detected and measured accurately without fibrillatory wave contamination.Having established a validated algorithm to measure the waveform features, the second aim was to use this algorithm to investigate the effect of beat interval dependency on the repolarisation waves, especially U waves, during AF and to compare them to those in sinus rhythm (SR) of healthy subjects. The research could provide mechanistic insight into the origin of U waves since AF is unique in its rapidly changing ventricular beat intervals. The preceding beat interval has a direct impact on ventricular filling dynamics and hence also on mechano-electrical coupling, one of the leading hypotheses of U wave genesis.Algorithms were developed to remove the contaminating fibrillatory waves in AF recordings and to measure features of the ventricular repolarisation waves.The ventricular repolarisation features, U and T waves, are measurable and dependent on preceding beat interval in AF and SR. The beat interval dependency of repolarisation features, especially the U wave, supported the mechano-electrical hypothesis during AF and SR.The research provides tools to facilitate the detection and reporting of U waves and their abnormalities in AF patients and provides mechanistic insight into rate dependency of ventricular repolarisation features

Ventricular divergence correlates with epicardial wavebreaks and predicts ventricular arrhythmia in isolated rabbit hearts during therapeutic hypothermia

INTRODUCTION: High beat-to-beat morphological variation (divergence) on the ventricular electrogram during programmed ventricular stimulation (PVS) is associated with increased risk of ventricular fibrillation (VF), with unclear mechanisms. We hypothesized that ventricular divergence is associated with epicardial wavebreaks during PVS, and that it predicts VF occurrence. METHOD AND RESULTS: Langendorff-perfused rabbit hearts (n = 10) underwent 30-min therapeutic hypothermia (TH, 30°C), followed by a 20-min treatment with rotigaptide (300 nM), a gap junction modifier. VF inducibility was tested using burst ventricular pacing at the shortest pacing cycle length achieving 1:1 ventricular capture. Pseudo-ECG (p-ECG) and epicardial activation maps were simultaneously recorded for divergence and wavebreaks analysis, respectively. A total of 112 optical and p-ECG recordings (62 at TH, 50 at TH treated with rotigaptide) were analyzed. Adding rotigaptide reduced ventricular divergence, from 0.13±0.10 at TH to 0.09±0.07 (p = 0.018). Similarly, rotigaptide reduced the number of epicardial wavebreaks, from 0.59±0.73 at TH to 0.30±0.49 (p = 0.036). VF inducibility decreased, from 48±31% at TH to 22±32% after rotigaptide infusion (p = 0.032). Linear regression models showed that ventricular divergence correlated with epicardial wavebreaks during TH (p<0.001). CONCLUSION: Ventricular divergence correlated with, and might be predictive of epicardial wavebreaks during PVS at TH. Rotigaptide decreased both the ventricular divergence and epicardial wavebreaks, and reduced the probability of pacing-induced VF during TH

Prediction of postoperative atrial fibrillation using the electrocardiogram: A proof of concept

Hospital patients recovering from major cardiac surgery are at high risk of postoperative atrial fibrillation (POAF), an arrhythmia which can be life-threatening. With the development of a tool to predict POAF early enough, the development of the arrhythmia could be potentially prevented using prophylactic treatments, thus reducing risks and hospital costs. To date, no reliable method suitable for autonomous clinical integration has been proposed yet. This thesis presents a study on the prediction of POAF using the electrocardiogram. A novel P-wave quality assessment tool to automatically identify high-quality P-waves was designed, and its clinical utility was assessed. Prediction of paroxysmal atrial fibrillation (AF) was performed by implementing and improving a selection of previously proposed methods. This allowed to perform a systematic comparison of those methods, and to test if their combination improved prediction of AF. Finally, prediction of POAF was tested in a clinically relevant scenario. This included studying the 48 hours preceding POAF, and automatically excluding noise-corrupted P-waves using the quality assessment tool. The P-wave quality assessment tool identified high-quality P-waves with high sensitivity (0.93) and good specificity (0.84). In addition, this tool improved the ability to predict AF, since it improved the precision of P-wave measurements. The best predictors of AF and POAF were measurements of the variability in P-wave time- and morphological features. Paroxysmal AF could be predicted with high specificity (0.93) and good sensitivity (0.82) when several predictors were combined. Furthermore, POAF could be predicted 48 hours before its onset with good sensitivity (0.74) and specificity (0.70). This leaves time for prophylactic treatments to be administered and possibly prevent POAF. Despite being promising, further work is required for these techniques to be useful in the clinical setting

Personalized Multi-Scale Modeling of the Atria: Heterogeneities, Fiber Architecture, Hemodialysis and Ablation Therapy

This book targets three fields of computational multi-scale cardiac modeling. First, advanced models of the cellular atrial electrophysiology and fiber orientation are introduced. Second, novel methods to create patient-specific models of the atria are described. Third, applications of personalized models in basic research and clinical practice are presented. The results mark an important step towards the patient-specific model-based atrial fibrillation diagnosis, understanding and treatment

Cardiac Inter Beat Interval and Atrial Fibrillation Detection using Video Plethysmography

Facial videoplethysmography provides non-contact measurement of heart activity based on blood volume pulsations detected in facial tissue. Typically, the signal is extracted using a simple webcam followed by elaborated signal processing methods, and provides limited accuracy of time-domain characteristics. In this study, we explore the possibility of providing accurate time-domain pulse and inter-beat interval measurements using a high- quality image sensor camera and various signal processing approaches, and use these measurements to diagnose atrial fibrillation. We capture synchronized signals using a high- quality camera, a simple webcam, an earlobe photoplethysmography sensor, and a body- surface electrocardiogram from a large group of subjects, including subjects diagnosed with cardiac arrhythmias. All signals are processed using both blind source separation and color conversion. We then assess accuracy of IBI detection, heart rate variability estimation, and atrial fibrillation diagnose by comparing to a body-surface electrocardiogram. We present a new heart variability indicator for blood volume pulsating signals. Our results demonstrate that the accuracy of a facial VPG system is greatly improved when using a high-quality camera. Coupling the high-quality camera with color conversion from RGB to Hue provides a level of accuracy equivalent to that of commercially available photoplethysmography sensors, and offers a non-contact alternative to current technology for heart rate variability assessment and atrial fibrillation screening

Applications of Vectorcardiography for Diagnosis and Risk Stratification in Subpopulations at Risk for Life-Threatening Arrhythmias

Introduction: Vectorcardiography, or 3-dimensional electrocardiography is a tool which can be used to identify subtle changes in the electrical forces of the heart, and which can be applied to atrial depolarization, ventricular depolarization and ventricular repolarization for prognostic and diagostic purposes. Methods: Kor’s regression-related and quasi orthogonal methods was used to derive vectorcardiographic parameters from the 12-lead electrocardiogram and applied to a cohort of cryptogenic stroke patients to assess atrial fibrillation, hypertrophic cardiomyopathy patients to assess for ventricular arrhythmias, applied with right-precordial directed quasi orthogonal method to arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC/D) patients for diagnosis, and applied to ventricular repolarization only to patients with genotype-positive/phenotype-negative Long QT2 syndrome (KNCH2 mutation) to assess for cardiac events. Parametric and non-parameteric parameters were presented as mean ± standard deviation and median (1st to 3rd interquartile ranges). Pearson and Spearman correlation coefficients were used for parametric and non-parametric data, respectively. Odds ratios with univariate and multivariate analyses as well as hazard ratios and Kaplan-Meier curves are presented. P-values under 0.05 were represented as significant. Results: In cryptogenic stroke patients, first atrial fibrillation event was predicted by baseline P-wave duration divided by P-wave vector magnitude (p<0.05). In hypertrophic cardiomyopathy patients, the spatial peaks QRS-T angle differentiated sustained ventricular arrhythmias (VA) from no VA (P < 0.001) and at 124.1 degrees gave positive and negative predictive values and an odds ratio of 36.7%, 96.1%, and 14.2 (95% confidence interval: 3.1-65.6), respectively. Combined right precordial-directed parameters were able to identify ARVD/C patients who otherwise met criteria but did not meet any ECG-specfiic 2010 Taskforce criteria from controls with a positive predictive value of 90.0% and negative predictive value of 83.3%. In patients with genotype positive KCNH2 mutations, without prolongation of the QTc, when dichotomized by the median of 0.30 mV, a low T-wave vector magnitude (TwVM) was associated with elevated cardiac event risk compared to those with high TwVM (HR=2.55, 95%CI 1.07-6.04, p=0.034) and the genotype-negative family members (HR=2.64, 95%CI 1.64-4.24, p<0.001). Conclusion: Vector magnitudes and spatial angles, involving atrial and ventricular depolarization as well as ventricular repolarization, can be helpful in identifying disease as well as first-onset arrhythmia in subpopulations at risk for sudden death or stroke

Relationship between body surface potential maps and atrial electrograms in patients with atrial fibrillation

PhD ThesisAtrial fibrillation (AF) is the most common cardiac arrhythmia. It is distinguished by fibrillating or trembling of the atrial muscle instead of normal contraction. Patients in AF have a much higher risk of stroke. AF is often driven by the left atrium (LA) and the diagnosis of AF is normally made from lead V1 in a 12-lead electrocardiogram (ECG). However, lead V1 is dominated by right atrial activity due to its proximal location to the right atrium (RA). Consequently it is not well understood how electrical activity from the LA contributes to the ECG. Studies of the AF mechanisms from the LA are typically based on invasive recording techniques. From a clinical point of view it is highly desirable to have an alternative, non-invasive characterisation of AF. The aim of this study was to investigate how the LA electrical activity was expressed on the body surface, and if it could be observed preferentially in different sites on the body surface. For this purpose, electrical activity of the heart from 20 patients in AF were recorded simultaneously using 64-lead body surface potential mapping (BSPM) and bipolar 10-electrode catheters located in the LA and coronary sinus (CS). Established AF characteristics such as amplitude, dominant frequency (DF) and spectral concentration (SC) were estimated and analysed. Furthermore, two novel AF characteristics (intracardiac DF power distribution, and body surface spectral peak type) were proposed to investigate the relationship between the BSPM and electrogram (EGM) recordings. The results showed that although in individual patients there were body surface sites that preferentially represented the AF characteristics estimated from the LA, those sites were not consistent across all patients. It was found that the left atrial activity could be detected in all body surface sites such that all sites had a dominant or non-dominant spectral peak corresponding to EGM DF. However, overall the results suggested that body surface site 22 (close to lead V1) was more closely representative of the CS activity, and site 49 (close to the posterior lower central right) was more closely representative of the left atrial activity. There was evidence of more accurate estimation of AF characteristics using additional electrodes to lead V1