Can 24 h of ambulatory ECG be used to triage patients to extended monitoring?

BACKGROUND: Access to long-term ambulatory recording to detect atrial fibrillation (AF) is limited for economical and practical reasons. We aimed to determine whether 24 h ECG (24hECG) data can predict AF detection on extended cardiac monitoring. METHODS: We included all US patients from 2020, aged 17-100 years, who were monitored for 2-30 days using the PocketECG device (MEDICALgorithmics), without AF ≥30 s on the first day (n = 18,220, mean age 64.4 years, 42.4% male). The population was randomly split into equal training and testing datasets. A Lasso model was used to predict AF episodes ≥30 s occurring on days 2-30. RESULTS: The final model included maximum heart rate, number of premature atrial complexes (PACs), fastest rate during PAC couplets and triplets, fastest rate during premature ventricular couplets and number of ventricular tachycardia runs ≥4 beats, and had good discrimination (ROC statistic 0.7497, 95% CI 0.7336-0.7659) in the testing dataset. Inclusion of age and sex did not improve discrimination. A model based only on age and sex had substantially poorer discrimination, ROC statistic 0.6542 (95% CI 0.6364-0.6720). The prevalence of observed AF in the testing dataset increased by quintile of predicted risk: 0.4% in Q1, 2.7% in Q2, 6.2% in Q3, 11.4% in Q4, and 15.9% in Q5. In Q1, the negative predictive value for AF was 99.6%. CONCLUSION: By using 24hECG data, long-term monitoring for AF can safely be avoided in 20% of an unselected patient population whereas an overall risk of 9% in the remaining 80% of the population warrants repeated or extended monitoring.

Signal Quality Assessment of a Novel ECG Electrode for Motion Artifact Reduction.

BACKGROUND: The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. METHODS: The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time-frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. RESULTS: The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. CONCLUSIONS: The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring.

Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping.

A graph based framework for fMRI brain activation mapping is presented. The approach exploits the spectral graph wavelet transform (SGWT) for the purpose of defining an advanced multi-resolutional spatial transformation for fMRI data. The framework extends wavelet based SPM (WSPM), which is an alternative to the conventional approach of statistical parametric mapping (SPM), and is developed specifically for group-level analysis. We present a novel procedure for constructing brain graphs, with subgraphs that separately encode the structural connectivity of the cerebral and cerebellar gray matter (GM), and address the inter-subject GM variability by the use of template GM representations. Graph wavelets tailored to the convoluted boundaries of GM are then constructed as a means to implement a GM-based spatial transformation on fMRI data. The proposed approach is evaluated using real as well as semi-synthetic multi-subject data. Compared to SPM and WSPM using classical wavelets, the proposed approach shows superior type-I error control. The results on real data suggest a higher detection sensitivity as well as the capability to capture subtle, connected patterns of brain activity.

Noninvasive Assessment of Atrioventricular Nodal Function: Effect of Rate-Control Drugs during Atrial Fibrillation.

BACKGROUND: During atrial fibrillation (AF), conventional electrophysiological techniques for assessment of refractory period or conduction velocity of the atrioventricular (AV) node cannot be used. We aimed at evaluating changes in AV nodal properties during administration of tecadenoson and esmolol using a novel ECG-based method. METHODS: Fourteen patients (age 58 ± 8 years, 10 men) with AF were randomly assigned to either 75 or 300 µg intravenous tecadenoson. After tecadenoson wash-out, patients received esmolol continuously (100 µg/kg per min for 10 mins, then 50 µg/kg per min for 50 mins). Atrial fibrillatory rate (AFR) and heart rate (HR) were assessed in 15-min segments. Using the novel method, we assessed the absolute refractory periods of the slow and fast pathways (aRPs and aRPf) of the AV node to produce an estimate of the functional refractory period. RESULTS: During esmolol infusion, AFR and HR were significantly decreased and the absolute refractory period was significantly prolonged in both pathways (aRPs: 387 ± 73 vs 409 ± 62 ms, P < 0.05; aRPf: 490 ± 80 vs 529 ± 58 ms, P < 0.05). During both tecadenoson doses, HR decreased significantly and AFR was unchanged. Both aRPs and aRPf were prolonged for a 75 µg dose (aRPs: 322 ± 97 vs 476 ± 75 ms, P < 0.05; aRPf: 456 ± 102 vs 512 ± 55 ms, P < 0.05) whereas a trend toward prolongation was observed for a 300 µg dose. CONCLUSIONS: The estimated parameters reflect expected changes in AV nodal properties, i.e., slower conduction through the AV node for tecadenoson and prolongation of the AV node refractory period for esmolol. Thus, the proposed approach may be used to assess drug effects on the AV node in AF patients.

Noninvasive characterization of atrioventricular conduction in patients with atrial fibrillation.

The atrioventricular (AV) node plays a fundamental role in patients with atrial fibrillation (AF), acting as a filter to the numerous irregular atrial impulses which bombard the node. A phenomenological approach to better understand AV nodal electrophysiology is to analyze the ventricular response with respect to irregularity. In different cohorts of AF patients, such analysis has been performed with the aim to evaluate the association between ventricular response characteristics and long-term clinical outcome and to determine whether irregularity is affected by rate-control drugs. Another approach to studying AV nodal characteristics is to employ a mathematical model which accounts for the refractory periods of the two AV nodal pathways. With atrial fibrillatory rate and RR intervals as input, the model has been considered for analyzing data during (i) rest and head-up tilt test, (ii) tecadenoson and esmolol, and (iii) rate-control drugs. The present paper provides an overview of our recent work on the characterization and assessment of AV nodal conduction using these two approaches.

Non-invasive assessment of the effect of beta blockers and calcium channel blockers on the AV node during permanent atrial fibrillation.

AIM: We aimed at assessing changes in AV nodal properties during administration of the beta blockers metoprolol and carvedilol, and the calcium channel blockers diltiazem and verapamil from electrocardiographic data. METHODS: Parameters accounting for the functional refractory periods of the slow and fast pathways (aRPs and aRPf) were estimated using atrial fibrillatory rate (AFR) and ventricular response assessed from 15-min ECG segments recorded at baseline and on drug treatment from sixty patients with permanent AF. RESULTS: The results showed that AFR and HR were significantly reduced for all drugs, and that aRPs and aRPf were significantly prolonged in both pathways. The prolongation in aRP was significantly larger for the calcium channel blockers than for the beta blockers. CONCLUSIONS: The changes observed in the AV node parameters are in line with the results of previous electrophysiological studies performed in patients during sinus rhythm, therefore supporting the clinical value of the method.

Atrial fibrillatory rate in the clinical context: natural course and prediction of intervention outcome.

Shortening of atrial refractory period during atrial fibrillation has been considered a hallmark of atrial electrical remodelling. The atrial fibrillatory cycle length, which is intimately related to the atrial fibrillatory rate (AFR), is generally accepted as a surrogate marker for local refractoriness. The value of using AFR to monitor the progress of atrial ablation therapy has been demonstrated and gradual slowing of AFR has consistently been observed to precede arrhythmia termination during paroxysmal or permanent atrial fibrillation ablation. Today, AFR is the key characteristic of the fibrillatory process, repeatedly validated against intracardiac recordings and extensively studied in clinical contexts. This paper provides an overview of clinical data accumulated since the method was introduced in 1998, and to present the current state of knowledge regarding ECG-derived AFR: its time course and dynamics, clinical factors affecting AFR, and available evidence of its value in the clinical context. We conclude that AFR is a promising, easily available AF characteristic that can be derived from the conventional surface ECG. It is clearly a useful tool for monitoring drug effects. Reference values for predicting intervention effect, however, are likely to be population- and context-specific and related to age, clinical types of atrial fibrillation, as well as to presence and advancement of underlying structural heart disease. Prospective studies in homogeneous patient populations are still needed to establish the clinical value of AFR.

Non-invasive evaluation of the effect of metoprolol on the atrioventricular node during permanent atrial fibrillation.

AIMS: During atrial fibrillation (AF), conventional electrophysiological techniques for assessment of refractory period or conduction velocity of the atrioventricular (AV) node cannot be used. We aimed at evaluating changes in AV nodal properties during administration of metoprolol from electrocardiogram data, and to support our findings with simulated data based on results from an electrophysiological study. METHODS AND RESULTS: Sixty patients (age 71 ± 9 years, 42 men) with permanent AF were included in the RATe control in Atrial Fibrillation (RATAF) study. Two 15 min segments, during baseline and metoprolol administration, starting at 2 pm were analysed in this study. Atrial fibrillatory rate (AFR), heart rate (HR), and AV nodal parameters were assessed. The AV nodal parameters account for the probability of an impulse not taking the fast pathway, the absolute refractory periods of the slow and fast pathways (aRPs and aRPf), representing the functional refractory period, and their respective prolongation in refractory period. In addition, simulated RR series were generated that mimic metoprolol administration through prolonged AV conduction interval and AV node effective refractory period. During metoprolol administration, AFR and HR were significantly decreased and aRP was significantly prolonged in both pathways (aRPs: 337 ± 60 vs. 398 ± 79 ms, P < 0.01; aRPf: 430 ± 91 vs. 517 ± 100 ms, P < 0.01). Similar results were found for the simulated RR series, both aRPs and aRPf being prolonged with metoprolol (aRPs: 413 ± 33 vs. 437 ± 43 ms, P = 0.01; aRPf: 465 ± 40 vs. 502 ± 69 ms, P = 0.02). CONCLUSION: The AV nodal parameters reflect expected changes after metoprolol administration, i.e. a prolongation in functional refractory period. The simulations confirmed that aRPs and aRPf may serve as an estimate of the functional refractory period.

The STAFF III ECG database and its significance for methodological development and evaluation.

The development of new techniques for detection and characterization of transient myocardial ischemia has benefited considerably from the STAFF III database, acquired in patients receiving elective prolonged percutaneous transluminal coronary angiography. The present article reviews a range of techniques developed and/or evaluated on the ECG signals of this database, including techniques for exploring abnormal intra-QRS potentials, QRS slopes, QRS angles, T wave morphology, T wave alternans, spatiotemporal ECG information, as well as heart rate dynamics. The detection of changes in body position is also briefly reviewed as it is intimately related to ischemia detection.

Rapid slowing of the atrial fibrillatory rate after administration of AZD7009 predicts conversion of atrial fibrillation.

BACKGROUND: Effects on the atrial fibrillatory rate (AFR) were studied during infusion with the combined potassium and sodium channel blocker AZD7009. METHODS AND RESULTS: Patients with persistent atrial fibrillation (AF) were randomized to AZD7009 or placebo. Thirty-five patients converted to sinus rhythm (SR) and were matched to 35 non-converters. The mean AFR before conversion was 231 fibrillations per minute (fpm), having decreased by 41%; in non-converters, it was 296 fpm at the end of infusion, having decreased by 26%. The rate of decrease was greater in converters at 5 min, -88 vs. -66 fpm (p=0.02), and at 10 min, -133 vs. -111 fpm (p=0.048). The AFR-SD and the exponential decay decreased. A small left atrial area was the only baseline predictor of conversion to SR. CONCLUSIONS: AZD7009 produced a significantly more rapid decrease of the AFR in converters than in non-converters, but the AFR at baseline was not predictive of conversion.

Characterization of Atrial Fibrillation Episode Patterns: A Comparative Study.

OBJECTIVE: The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors. METHODS: To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the intraclass correlation coefficient are used to describe agreement and reliability, respectively. The parameters are studied on two PhysioNet databases with annotated AF episodes, also accounting for shutdowns due to poor signal quality. RESULTS: The agreement is similar for both parameters when computed for detector-based and annotated patterns, which is 0.80 for AF aggregation and 0.85 for AF density. On the other hand, the reliability differs substantially, with 0.96 for AF aggregation but only 0.29 for AF density. This finding suggests that AF aggregation is considerably less sensitive to detection errors. The results from comparing three strategies to handle shutdowns vary considerably, with the strategy that disregards the shutdown from the annotated pattern showing the best agreement and reliability. CONCLUSIONS: Due to its better robustness to detection errors, AF aggregation should be preferred. To further improve performance, future research should put more emphasis on AF pattern characterization.

Performance Evaluation of QT-RR Adaptation Time Lag Estimation in Exercise Stress Testing.

BACKGROUND: Slower adaptation of the QT interval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag. METHODS: The time lag estimation is based on the delay between the observed QT intervals and the QT intervals derived from the observed RR intervals using a memoryless transformation. Assuming that the two types of QT interval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time lag estimators are derived. Estimation performance is evaluated using an ECG simulator which models change in RR and QT intervals with a known time lag, muscle noise level, respiratory rate, and more. The accuracy of T-wave end delineation and the influence of the learning window positioning for model parameter estimation are also investigated. RESULTS: Using simulated datasets, the results show that the proposed approach to estimation can be applied to any changes in heart rate trend as long as the frequency content of the trend is below a certain frequency. Moreover, using a proper position of the learning window for exercise so that data compensation reduces the effect of nonstationarity, a lower mean estimation error results for a wide range of time lags. Using a clinical dataset, the Laplacian-based estimator shows a better discrimination between patients grouped according to the risk of suffering from coronary artery disease. CONCLUSIONS: Using simulated ECGs, the performance evaluation of the proposed method shows that the estimated time lag agrees well with the true time lag.

Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening.

OBJECTIVE: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT. METHODS: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable. RESULTS: The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. CONCLUSION: The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector.Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.

RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.

INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. METHODS: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. RESULTS: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91-0.94 in RBDB and 0.93 in SHDB, compared to 0.89-0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.

Decrease of the atrial fibrillatory rate, increased organization of the atrial rhythm and termination of atrial fibrillation by AZD7009.

BACKGROUND: The atrial fibrillatory rate (AFR), on AZD7009 as compared to placebo, was investigated as a potential biomarker for electrophysiological effect in early antiarrhythmic drug development. METHODS: Patients with permanent AF received infusions of AZD7009 and placebo in an exploratory two-way, single-blind, randomized cross-over study. The ECG was continuously recorded, and following QRST cancellation the AFR, its standard deviation (SD), the exponential decay and the atrial electrogram amplitude were determined as 3-min averages. RESULTS: The mean AFR rapidly decreased by 43% from baseline (394 ± 38 to 225 ± 61 fibrillations/min, p=0.0003) on AZD7009, but not on placebo. The SD of the AFR and the exponential decay decreased in parallel. In 2 of 8 patients, termination of AF occurred after the AFR had decreased by 58% and 53%, respectively. CONCLUSIONS: The AFR may potentially serve as a biomarker of electrophysiological effects in early evaluation of rhythm control agents.

Atrial fibrillation episode patterns as predictor of clinical outcome of catheter ablation.

Methods for characterization of atrial fibrillation (AF) episode patterns have been introduced without establishing clinical significance. This study investigates, for the first time, whether post-ablation recurrence of AF can be predicted by evaluating episode patterns. The dataset comprises of 54 patients (age 56 ± 11 years; 67% men), with an implantable cardiac monitor, before undergoing the first AF catheter ablation. Two parameters of the alternating bivariate Hawkes model were used to characterize the pattern: AF dominance during the monitoring period (log(mu)) and temporal aggregation of episodes (beta1). Moreover, AF burden and AF density, a parameter characterizing aggregation of AF burden, were studied. The four parameters were computed from an average of 29 AF episodes before ablation. The risk of AF recurrence after catheter ablation using the Hawkes parameters log(mu) and beta1, AF burden, and AF density was evaluated. While the combination of AF burden and AF density is related to a non-significant hazard ratio, the combination of log(mu) and beta1 is related to a hazard ratio of 1.95 (1.03-3.70; p < 0.05). The Hawkes parameters showed increased risk of AF recurrence within 1 year after the procedure for patients with high AF dominance and high episode aggregation and may be used for pre-ablation risk assessment.

ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions.

The present article proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance.

QT Interval Adaptation to Heart Rate Changes in Atrial Fibrillation as a Predictor of Sudden Cardiac Death.

OBJECTIVE: The clinical significance of QT interval adaptation to heart rate changes has been poorly investigated in atrial fibrillation (AF), since QT delineation in the presence of f-waves is challenging. The objective of the present study is to investigate new techniques for QT adaptation estimation in permanent AF. METHODS: A multilead strategy based on periodic component analysis, to emphasize T-wave periodicity, is proposed for QT delineation. QT adaptation is modeled by a linear, time-invariant filter, which describes the dependence between the current QT interval and the preceding RR intervals, followed by a memoryless, nonlinear, function. The QT adaptation time lag is determined from the estimated impulse response. RESULTS: Using simulated ECGs in permanent AF, the transformed lead was found to offer more accurate QT delineation and time lag estimation than did the original ECG leads for a wide range of f-wave amplitudes. In a population with chronic heart failure and permanent AF, the time lag estimated from the transformed lead was found to have the strongest, statistically significant association with sudden cardiac death (SCD) (hazard ratio = 3.49). CONCLUSIONS: Periodic component analysis provides more accurate QT delineation and improves time lag estimation in AF. A prolonged QT adaptation time lag is associated with a high risk for SCD. SIGNIFICANCE: SCD risk markers originally developed for sinus rhythm can also be used in AF, provided that T-wave periodicity is emphasized. The time lag is a potentially useful biomarker for identifying patients at risk for SCD, guiding clinicians in adopting effective therapeutic decisions.

Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors.

The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time-frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.