Focal and pseudo/rotational activations in human atrial fibrillation defined with automated periodicity mapping.

INTRODUCTION: Defining atrial fibrillation (AF) wave propagation is challenging unless local signal features are discrete or periodic. Periodic focal or rotational activity may identify AF drivers. Our objective was to characterize AF propagation at sites with periodic activation to evaluate the prevalence and relationship between focal and rotational activation. METHODS: We included 80 patients (61 ± 10 years, persistent AF 49%) from the FaST randomized trial that compared the efficacy of adjunctive focal site ablation versus pulmonary vein isolation. Patients underwent left atrial (LA) activation mapping with a 20-pole circular catheter during spontaneous or induced AF. Five-second bipolar and unipolar electrograms in AF were analyzed. Periodic sites were identified by spectral analysis of the bipolar electrogram. Activation maps of periodic sites were constructed using an automated, validated tracking algorithm, and classified into three patterns: focal sites (FS), rotation (RO), or pseudo-rotation (pRO). RESULTS: The most common propagation pattern at periodic sites was FS for 5-s in all patients (4.9 ± 1.9 per patient). RO and pRO were observed in two and seven patients, respectively, but were all transient (3-5 cycles). Activation from a FS evolved into transient RO/pRO in five patients. No patient had autonomous RO/pRO activations. Patients with RO/pRO had greater LA surface area with periodicity (78 ± 7 vs. 63 ± 16%, p = .0002) and shorter LA periodicity CL (166 ± 10 vs. 190±28 ms, p = .0001) than the rest. CONCLUSION: Using automated, regional AF periodicity mapping, FS is more prevalent and temporally stable than RO/pRO. Most RO/pRO evolve from neighboring FS. These findings and their implications for AF maintenance require verification with global, panoramic mapping.

Unipolar electrogram-based voltage mapping with far-field cancellation to improve detection of abnormal atrial substrate during atrial fibrillation.

INTRODUCTION: An important substrate for atrial fibrillation (AF) is fibrotic atrial myopathy. Identifying low voltage, myopathic regions during AF using traditional bipolar voltage mapping is limited by the directional dependency of wave propagation. Our objective was to evaluate directionally independent unipolar voltage mapping, but with far-field cancellation, to identify low-voltage regions during AF. METHODS: In 12 patients undergoing pulmonary vein isolation for AF, high-resolution voltage mapping was performed in the left atrium during sinus rhythm and AF using a roving 20-pole circular catheter. Bipolar electrograms (EGMs) (Bi) < 0.5 mV in sinus rhythm identified low-voltage regions. During AF, bipolar voltage and unipolar voltage maps were created, the latter with (uni-res) and without (uni-orig) far-field cancellation using a novel, validated least-squares algorithm. RESULTS: Uni-res voltage was ~25% lower than uni-orig for both low voltage and normal atrial regions. Far-field EGM had a dominant frequency (DF) of 4.5-6.0 Hz, and its removal resulted in a lower DF for uni-orig compared with uni-res (5.1 ± 1.5 vs. 4.8 ± 1.5 Hz; p < .001). Compared with Bi, uni-res had a significantly greater area under the receiver operator curve (0.80 vs. 0.77; p < .05), specificity (86% vs. 76%; p < .001), and positive predictive value (43% vs. 30%; p < .001) for detecting low-voltage during AF. Similar improvements in specificity and positive predictive value were evident for uni-res versus uni-orig. CONCLUSION: Far-field EGM can be reliably removed from uni-orig using our novel, least-squares algorithm. Compared with Bi and uni-orig, uni-res is more accurate in detecting low-voltage regions during AF. This approach may improve substrate mapping and ablation during AF, and merits further study.

P-Wave Duration/Amplitude Ratio Quantifies Atrial Low-Voltage Area and Predicts Atrial Arrhythmia Recurrence After Pulmonary Vein Isolation.

BACKGROUND: Atrial low-voltage areas (LVAs) in patients with atrial fibrillation increase the risk of atrial arrhythmia (AA) recurrence after pulmonary vein isolation (PVI). Contemporary LVA prediction scores (DR-FLASH, APPLE) do not include P-wave metrics. We aimed to evaluate the utility of P-wave duration/amplitude ratio (PWR) in quantifying LVA and predicting AA recurrence after PVI. METHODS: In 65 patients undergoing first-time PVI, 12-lead ECGs were recorded during sinus rhythm. PWR was calculated as the ratio between the longest P-wave duration and P-wave amplitude in lead I. High-resolution biatrial voltage maps were collected and LVAs included bipolar electrogram amplitudes < 0.5 mV or < 1.0 mV. An LVA quantification model was created with the use of clinical variables and PWR, and then validated in a separate cohort of 24 patients. Seventy-eight patients were followed for 12 months to evaluate AA recurrence. RESULTS: PWR strongly correlated with left atrial (LA) (< 0.5 mV: r = 0.60; < 1.0 mV: r = 0.68; P < 0.001) and biatrial LVA (< 0.5 mV: r = 0.63; < 1.0 mV: r = 0.70; P < 0.001). Addition of PWR to clinical variables improved model quantification of LA LVA at the < 0.5 mV (adjusted R2 = 0.59 to 0.68) and < 1.0 mV (adjusted R2 = 0.59 to 0.74) cutoffs. In the validation cohort, PWR model-predicted LVA correlated strongly with measured LVA (< 0.5 mV: r = 0.78; < 1.0 mV: r = 0.81; P < 0.001). PWR model was superior to DR-FLASH (area under the receiver operating characteristic curve [AUC] 0.90 vs 0.78; P = 0.030) and APPLE (AUC 0.90 vs 0.67; P = 0.003) at detecting LA LVA and similar at predicting AA recurrence after PVI (AUC 0.67 vs 0.65 and 0.60). CONCLUSION: Our novel PWR model accurately quantifies LVA and predicts AA recurrence after PVI. PWR model-predicted LVA may help guide patient selection for PVI.

Deep Learning Classification of Unipolar Electrograms in Human Atrial Fibrillation: Application in Focal Source Mapping.

Focal sources are potential targets for atrial fibrillation (AF) catheter ablation, but they can be time-consuming and challenging to identify when unipolar electrograms (EGM) are numerous and complex. Our aim was to apply deep learning (DL) to raw unipolar EGMs in order to automate putative focal sources detection. We included 78 patients from the Focal Source and Trigger (FaST) randomized controlled trial that evaluated the efficacy of adjunctive FaST ablation compared to pulmonary vein isolation alone in reducing AF recurrence. FaST sites were identified based on manual classification of sustained periodic unipolar QS EGMs over 5-s. All periodic unipolar EGMs were divided into training (n = 10,004) and testing cohorts (n = 3,180). DL was developed using residual convolutional neural network to discriminate between FaST and non-FaST. A gradient-based method was applied to interpret the DL model. DL classified FaST with a receiver operator characteristic area under curve of 0.904 ± 0.010 (cross-validation) and 0.923 ± 0.003 (testing). At a prespecified sensitivity of 90%, the specificity and accuracy were 81.9 and 82.5%, respectively, in detecting FaST. DL had similar performance (sensitivity 78%, specificity 89%) to that of FaST re-classification by cardiologists (sensitivity 78%, specificity 79%). The gradient-based interpretation demonstrated accurate tracking of unipolar QS complexes by select DL convolutional layers. In conclusion, our novel DL model trained on raw unipolar EGMs allowed automated and accurate classification of FaST sites. Performance was similar to FaST re-classification by cardiologists. Future application of DL to classify FaST may improve the efficiency of real-time focal source detection for targeted AF ablation therapy.

Focal source and trigger mapping in atrial fibrillation: Randomized controlled trial evaluating a novel adjunctive ablation strategy.

BACKGROUND: Intraoperative mapping has demonstrated focal activations during human atrial fibrillation (AF). These putative AF sources can manifest sustained periodic bipolar and unipolar QS electrograms (EGMs). We have automated the detection of these EGM features using our validated Focal Source and Trigger (FaST) computational algorithm. OBJECTIVE: The purpose of this study was to conduct a randomized controlled pilot evaluating the feasibility and efficacy of FaST mapping/ablation as an adjunct to pulmonary vein isolation (PVI) in reducing AF recurrence. METHODS: We randomized 80 patients with high-burden paroxysmal or persistent AF (age 61 ± 10 years; 75% male) to PVI alone (n = 41) or PVI+FaST mapping/ablation (n = 39). The primary endpoint was time to AF recurrence >30 seconds between 3 and 12 months after 1 procedure. RESULTS: FaST sites were identified in all but 1 patient and were localized to pulmonary vein (PV) (2.1 ± 1.1 per patient) and extra-PV regions (2.8 ± 1.4 per patient). FaST mapping and ablation times were 27 ± 9 minutes and 8.5 ± 5 minutes, respectively. Patients with AF termination during ablation had greater AF cycle length prolongation with PVI+FaST than PVI (Δ20 ± 14 ms vs Δ5 ± 17 ms; P = .046). Freedom from AF recurrence at 12 months was higher in PVI+FaST vs PVI for patients off antiarrhythmic drugs (74% vs 51%; hazard ratio 0.48; 95% confidence interval 0.21-1.08; P = .064) but did not quite reach statistical significance. Major adverse events were similar between the 2 groups. CONCLUSION: In this randomized controlled pilot, real-time FaST mapping provided an intuitive, automated approach for localizing focal AF sources. FaST ablation as an adjunct to PVI may reduce AF recurrence, which requires verification with a larger multicenter trial.

Characterizing contrast origins and noise contribution in spin-echo EPI BOLD at 3 T.

In this work, we characterize contrast origins and noise contributions of spin echo (SE) EPI BOLD signal at 3 T. SE BOLD is a fMRI method of choice for imaging brain regions affected by susceptibility artifacts at lower fields, but its sensitivity remains a limiting factor for whole-brain imaging. To resolve this, the signal and noise contributions as well as TE dependence of SE EPI are characterized in this study. By integrating a two-compartment BOLD model with a physiological-thermal noise model, a new SE-BOLD signal model was introduced. The new SE-BOLD model was fit into SE-EPI fMRI data acquired during hypercapnic manipulations at various TEs, using typical fMRI voxel dimensions (3.4 × 3.4 × 5 mm3). Our model predicts intra- and extravascular signal and noise contributions consistent with our understanding of the SE-EPI contrast mechanism. The intravascular BOLD contribution is shown to dominate at TEs lower than tissue T2, but the physiological noise contributions in SE-EPI signal is also shown to be lower than that of gradient-echo (GE). Furthermore, SE-EPI contrast-to-noise ratio (CNR) is not maximized at tissue T2 as is typically assumed. To summarize, a new SE-BOLD model was proposed to characterize SE-BOLD contrast and physiological noise contribution at 3 T. Results suggests that SE-BOLD sensitivity can be improved by using shorter TEs, making it a more attractive choice for fMRI, especially in regions with susceptibility artifacts. Such optimizations could also help extend the application of SE BOLD to WM fMRI studies.

Spin-Echo Resting-State Functional Connectivity in High-Susceptibility Regions: Accuracy, Reliability, and the Impact of Physiological Noise.

Gradient-echo (GE) echo-planar imaging (EPI) is the method of choice in blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) studies, as it demonstrates substantially higher BOLD sensitivity than its spin-echo (SE) counterpart. However, it is also well known that the GE-EPI signal is prone to signal dropouts and shifts due to susceptibility effects near air-tissue interfaces. SE-EPI, in contrast, is minimally affected by these artifacts. In this study, we quantify, for the first time, the sensitivity and specificity of SE and GE EPI for resting-state fMRI functional connectivity (fcMRI) mapping, using the 1000-brain fcMRI atlas (Yeo et al., 2011 ) as the pseudoground truth. Moreover, we assess the influence of physiological processes on resting-state BOLD measured using both regular and ultrafast GE and SE acquisitions. Our work demonstrates that SE-EPI and GE-EPI are associated with similar sensitivities, specificities, and intersubject reproducibility in fcMRI for most brain networks, generated using both seed-based analysis and independent component analysis. More importantly, SE-based fcMRI measurements demonstrated significantly higher sensitivity, specificity, and intersubject reproducibility in high-susceptibility regions, spanning the limbic and frontal networks in the 1000-brain atlas. In addition, SE-EPI is significantly less sensitive to prominent sources of physiological noise, including low-frequency respiratory volume and heart rate variations. Our work suggests that SE-EPI should be increasingly adopted in the study of networks spanning susceptibility-affected brain regions, including those that are important to memory, language, and emotion.