Recent Advances in the Noninvasive Study of Atrial Conduction Defects Preceding Atrial Fibrillation

The P-wave represents the electrical activity in the electrocardiogram (ECG) associated with the heart\u27s atrial contraction. This wave has merited significant research efforts in recent years with the aim to characterize atrial depolarization from the ECG. Indeed, the alterations of the P-wave main time, frequency, and wavelet features have been widely studied to predict the onset of atrial fibrillation (AF), both spontaneously and after a specific treatment, such as pharmacological or electrical cardioversion, catheter ablation, as well as cardiac surgery. To this respect, the P-wave prolongation is today a clinically accepted marker of high risk of suffering AF. However, given the relatively low P-wave amplitude in the ECG, its analysis has been most widely carried out from signal-averaged ECG signals. Unfortunately, these kind of recordings are uncommon in routine clinical practice and, moreover, they obstruct the possibility of studying the information carried by each single P-wave as well as its variability over time. These limitations have motivated the recent development of the beat-to-beat P-wave analysis, which has proven to be very useful in revealing interesting information about the altered atrial conduction preceding the onset of AF. Within this context, the main goal of this chapter is to review the most recent advances reached by this kind of analysis in the noninvasive assessment of atrial conduction alterations. Thus, the chapter will introduce and discuss the existing methods of the beat-to-beat P-wave analysis and their application to predict the onset of AF as well as its advantages and disadvantages compared with the signal-averaged P-wave analysis

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices

Assessment of ventricular repolarization instability and cardiac risk stratification in different pathological and abnormal conditions

Cardiovascular diseases (CVDs) represents the leading cause of mortality worldwide [1,2]. These pathological conditions are mainly characterized by a structurally abnormal heart, that is, a vulnerable substrate, prone to the abnormal generation and/or propagation of the electrical impulse, determining the onset of ventricular arrhythmias, which can result in sudden cardiac death (SCD) [3]. In this context, the assessment of ventricular repolarization from the electrocardiogram (ECG) signal has been shown to provide with valuable information for risk stratification and several electrocardiographic indices have been proposed in the literature [4]. The main objective of this thesis is to propose methodological advances for the assessment of ventricular repolarization instability in pathological and abnormal conditions. These contributions are aimed at improving the prediction of ventricular arrhythmias and, consequently, better identifying SCD risk. In particular, we have addressed this objective by developing robust methodologies for the assessment of T-wave alternans (TWA) and ventricular repolarization instability, in invasive and non-invasive cardiac signals, that have been evaluated in both experimental and clinical conditions. In the first part of the thesis, TWA was simultaneously characterized (prevalence, magnitude, time-course, and alternans waveform) in body-surface ECG and intracardiac electrograms (EGMs) signals during coronary artery occlusion. Signals from both body surface ECG and intracardiac EGMs recorded from 4 different anatomical heart locations (coronary sinus, epicardial space and left and right ventricles) were analyzed following a multilead strategy. Leads were linearly combined using the periodic component analysis (πCA) [5], which maximizes the 2-beat periodicity (TWA periodicity) content present on the available leads. Then the Laplacian Likelihood Ratio method (LLRM) [6] was applied for TWA detection and estimation. A sensitivity study for TWA detection from the 5 different locations of leads was performed, revealing that it is the combination of the ECG leads that better performs. In addition, this multilead approach allowed us to find the optimal combination of intracardiac leads usable for in-vivo monitorization of TWA directly from an implantable device, with a sensitivity comparable to the ECG analysis. These results encourage further research to determine the feasibility of predicting imminent VT/VF episodes by TWA analysis implemented in implantable cardioverter defibrillator’s (ICD) technology.Then, we have studied the potential changes induced by a prolonged exposure to simulated microgravity on ventricular repolarization in structurally normal hearts. It is well known that this environmental condition affects the control of autonomic and cardiovascular systems [7], with a potential increase on cardiac electrical instability. The effects of short- (5 days), mid- (21 days) and long- (60 days) exposure to simulated microgravity on TWA using the head-down bed-rest (HDBR) model [8] were assessed. TWA was evaluated before (PRE), during and after (POST) the immobilization period, by the long-term averaging technique in ambulatory ECG Holter recordings [9]. Additionally, we proposed an adapted short-term averaging approach for shorter, non-stationary ECG signals obtained during two stress manoeuvres (head-up tilt-table and bicycle exercise tests). Both approaches are based on the multilead analysis used in the previous study. The absence of significant changes between PRE and POST-HDBR on TWA indices suggests that a long-term exposure to simulated microgravity is not enough to induce alterations in healthy myocardial substrate up to the point of reflecting electrical instability in terms of TWA on the ECG. Finally, methodological advances were proposed for the assessment of ventricular repolarization instability from the ECG signal in the presence of sporadic (ventricular premature contractions, VPCs) and sustained (atrial fibrillation) rhythm disturbances.On the one hand, a methodological improvement for the estimation of TWA amplitude in ambulatory ECG recordings was proposed, which deals with the possible phase reversal on the alternans sequence induced by the presence of VPCs [10]. The performance of the algorithm was first evaluated using synthetic signals. Then, the effect of the proposed method in the prognostic value of TWA amplitude was assessed in real ambulatory ECG recordings from patients with chronic heart failure (CHF). Finally, circadian TWA changes were evaluated as well as the prognostic value of TWA at different times of the day. A clinical study demonstrated the enhancement in the predictive value of the index of average alternans (IAA) [9] for SCD stratification. In addition, results suggested that alternans activity is modulated by the circadian pattern, preserving its prognostic information when computed just during the morning, which is also the day interval with the highest reported SCD incidence. Thus, suggesting that time of the day should be considered for SCD risk prediction. On the other hand, the high irregularity of the ventricular response in atrial fibrillation (AF) limits the use of the most common ECG-derived markers of repolarization heterogeneity, including TWA, under this clinical condition [11]. A new method for assessing ventricular repolarization changes based on a selective averaging technique was developed and new non-invasive indices of repolarization variation were proposed. The positive impact in the prognostic value of the computed indices was demonstrated in a clinical study, by analyzing ECG Holter recordings from CHF patients with AF. To the best of our knowledge, this is the first study that attempts a non-invasive SCD stratification of patients under AF rhythm by assessing ventricular repolarization instability from the ECG signal. To conclude, the research presented in this thesis sheds some light in the identification of pro-arrhythmic factors, which plays an important role in adopting efficient therapeutic strategies. In particular, the optimal configuration for real-time monitoring of repolarization alternans from intracardiac EGMs, together with the prognostic value of the proposed non-invasive indices of alternans activity and ventricular instability variations in case of AF rhythms demonstrated in two clinical studies, would increase the effectiveness of (ICD) therapy. Finally, the analysis of ECG signals recorded during HDBR experiments in structurally healthy hearts, also provides interesting information on cardiovascular alterations produced in immobilized or bedridden patients.<br /

CARDIAC RHYTHM DURING MECHANICAL VENTILATION AND WEANING FROM VENTILATION

The transition from mechanical ventilation (MV) to spontaneous ventilation during weaning is associated with hemodynamic alterations and autonomic nervous system (ANS) alterations (reflected by heart rate variability [HRV]). Although cardiac dysrhythmias are an important manifestation of hemodynamic alterations, development of dysrhythmias during MV and weaning and subsequent impact on length of MV has received little attention. The purposes of this dissertation were to 1) evaluate the relationship of heart rate variability (HRV) during weaning to the development of cardiac dysrhythmias and 2) determine the relationship of cardiac dysrhythmias to length of MV. A convenience sample of 35 patients (66.7% men; mean age 53.3 years) who required MV was enrolled in this study. Continuous 3-lead electrocardiographic data were collected for 24 hours at baseline during MV and for the first 2 hours during the initial weaning trial. HRV was evaluated using spectral power analysis. Twenty- seven patients out of 30 were exposed to a combination of pressure support (8-15 cm H2O) and continuous positive airway pressure 5 cm H2O during weaning trial. Three patients self- extubated and received supplemental oxygen through either a partial rebreathing or non-rebreathing mask. Low frequency (LF) power HRV decreased, while high frequency (HF) and very low frequency (VLF) power HRV did not change during weaning. Multiple regression analyses showed that LF and HF HRV were significant predictors of occurrence of ventricular and supraventricular ectopic beats during weaning, while VLF power predicted occurrence of ventricular ectopic beats only. The mean of occurrence of supraventricular ectopic beats per hour during weaning was double the mean at baseline, while the mean of ventricular ectopic beats per hour did not change. Mean number of supraventricular ectopic beats per hour during weaning was a significant predictor of length of MV. This dissertation has fulfilled an important gap in the evidence base for cardiac dysrhythmias during weaning from MV. Cardiac dysrhythmias and HRV alterations should be systemically evaluated during MV and weaning trials in order to decrease length of MV

Atrial fibrillation: a study of substrate and triggers

1. Introduction2. History and Overview of Atrial Fibrillation3. A New Fully Implantable Goat Model of Atrial Fibrillation4. The Profibdilatory Action of Verapamil is Not Prevented by Propafenone5. Repetitive Four-Week Periods of Atrial Electrical Remodelling Promote Stability of Atrial Fibrillation -Evidence for a Second Factor Independent of Atrial Refractoriness in the Self-Perpetuation of Atrial Fibrillation6. Atrial Ectopy - The Coupling Interval of Atrial Premature Beats Following DC Cardioversion of Persistent AF Predicts Subsequent Recurrence of AF7. Atrial Ectopy -Evidence for Reversal of Atrial Electrical Remodelling8. Prevalence and Significance of Focal Sources of Atrial Arrhythmia in Patients Undergoing Cardioversion of Persistent Atrial Fibrillation9. Changes in Heart Rate Variability Following Cardioversion of Persistent Atrial Fibrillation in Ma