Dataset Information

Time- and frequency-domain analyses of atrial fibrillation activation rate: the optical mapping reference.

ABSTRACT: Time- and frequency-domain estimates of activation rate have been proposed to guide atrial fibrillation (AF) ablation in patients, but their electrophysiological correlates are unclear.This study sought to examine the relative correlation of average electrical cycle length (CL) and dominant frequency (DF) during AF with reference optical mapping measures.Eight sheep hearts were Langendorff-perfused and superfused with oxygenated Tyrode solution inside a tank representing the human thorax. Optical mapping (DI-4-ANEPPS) of 4 × 4 cm2 in the left atrium was performed at 0.5 mm/pixel and 600 fps. A 20-pole catheter was placed in the optical field of view to acquire 1.2-kHz unipolar recordings by the EnSite NavX System (ENS; St. Jude Medical, St. Paul, MN) optimized for CL and DF calculation. During AF, 5-second-long simultaneous optical and electrical signals were analyzed for CL and DF.During pacing, DF measurements had fewer false results than CL (6.6% to 2.5% vs. 21.5% to 4.4% depending on filtering, P <.001). During AF in regions showing periodic waves on both sides of the catheter optical 1,000/CL versus DF correlation showed 95% confidence identity and was better than unipolar measurements in the ENS (adjusted R(2): 0.58879 vs. 0.12902; P < 10(-6)). DFs of unipolar signals correlated better than CLs with DFs of optical signals. Similarly, bipolar DF correlation with optical DF was not different from identity (P >.157), but the bipolar CL showed smaller identity with the optical CL (P <.0004).DF values of unipolar and bipolar signals correlate with those of optical signals better than CL values for the respective signals.

SUBMITTER: Berenfeld O

PROVIDER: S-EPMC3202688 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Time- and frequency-domain analyses of atrial fibrillation activation rate: the optical mapping reference.

Berenfeld Omer O Ennis Steve S Hwang Elliot E Hooven Brian B Grzeda Krzysztof K Mironov Sergey S Yamazaki Masatoshi M Kalifa Jérôme J Jalife José J

Heart rhythm 20110514 11

<h4>Background</h4>Time- and frequency-domain estimates of activation rate have been proposed to guide atrial fibrillation (AF) ablation in patients, but their electrophysiological correlates are unclear.<h4>Objective</h4>This study sought to examine the relative correlation of average electrical cycle length (CL) and dominant frequency (DF) during AF with reference optical mapping measures.<h4>Methods</h4>Eight sheep hearts were Langendorff-perfused and superfused with oxygenated Tyrode solutio ...[more]

PMID: 21699849

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Project description:BackgroundAtrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings.MethodsTemporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm2 resolution) and 64-electrode MEM (higher density or lower density with 3 and 9 mm2 resolution, respectively). Unipolar MEM and NIOM recordings were processed by Fourier transform analysis into 28 407 total Fourier spectra. Thirty-five features for machine learning were extracted from each Fourier spectrum.ResultsTargeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation, including driver periphery electrodes, were added to driver center annotation. Notably, f1-score for the binary classification of higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04, P<0.05). The trained algorithm correctly highlighted 86% of driver regions with higher density but only 80% with lower-density MEM arrays (81% for lower-density+higher-density arrays together).ConclusionsThe machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.

Project description:Experimental and clinical data demonstrate that atrial fibrillation (AF) maintenance in animals and groups of patients depends on localized reentrant sources localized primarily to the pulmonary veins (PVs) and the left atrium(LA) posterior wall in paroxysmal AF but elsewhere, including the right atrium (RA), in persistent AF. Moreover, AF can be eliminated by directly ablating AF-driving sources or "rotors," that exhibit high-frequency, periodic activity. The RADAR-AF randomized trial demonstrated that an ablation procedure based on a more target-specific strategy aimed at eliminating high frequency sites responsible for AF maintenance is as efficacious as and safer than empirically isolating all the PVs. In contrast to the standard ECG, global atrial noninvasive frequency analysis allows non-invasive identification of high-frequency sources before the arrival at the electrophysiology laboratory for ablation. Body surface potential map (BSPM) replicates the endocardial distribution of DFs with localization of the highest DF (HDF) and can identify small areas containing the high-frequency sources. Overall, BSPM had a sensitivity of 75% and specificity of 100% for capturing intracardiac EGMs as having LARA DF gradient. However, raw BSPM data analysis of AF patterns of activity showed incomplete and instable reentrant patterns of activation. Thus, we developed an analysis approach whereby a narrow band-pass filtering allowed selecting the electrical activity projected on the torso at the HDF, which stabilized the projection of rotors that potentially drive AF on the surface. Consequently, driving reentrant patterns ("rotors") with spatiotemporal stability during >70% of the AF time could be observed noninvasibly after HDF-filtering. Moreover, computer simulations found that the combination of BSPM phase mapping with DF analysis enabled the discrimination of true rotational patterns even during the most complex AF. Altogether, these studies show that the combination of DF analysis with phase maps of HDF-filtered surface ECG recordings allows noninvasive localization of atrial reentries during AF and further a physiologically-based rationale for personalized diagnosis and treatment of patients with AF.

Project description:Purpose: Identifying targets for catheter ablation remains challenging in persistent atrial fibrillation (persAF). The dominant frequency (DF) of atrial electrograms during atrial fibrillation (AF) is believed to primarily reflect local activation. Highest DF (HDF) might be responsible for the initiation and perpetuation of persAF. However, the spatiotemporal behavior of DF remains not fully understood. Some DFs during persAF were shown to lack spatiotemporal stability, while others exhibit recurrent behavior. We sought to develop a tool to automatically detect recurrent DF patterns in persAF patients. Methods: Non-contact mapping of the left atrium (LA) was performed in 10 patients undergoing persAF HDF ablation. 2,048 virtual electrograms (vEGMs, EnSite Array, Abbott Laboratories, USA) were collected for up to 5 min before and after ablation. Frequency spectrum was estimated using fast Fourier transform and DF was identified as the peak between 4 and 10 Hz and organization index (OI) was calculated. The HDF maps were identified per 4-s window and an automated pattern recognition algorithm was used to find recurring HDF spatial patterns. Dominant patterns (DPs) were defined as the HDF pattern with the highest recurrence. Results: DPs were found in all patients. Patients in atrial flutter after ablation had a single DP over the recorded time period. The time interval (median [IQR]) of DP recurrence for the patients in AF after ablation (7 patients) decreased from 21.1 s [11.8 49.7 s] to 15.7 s [6.5 18.2 s]. The DF inside the DPs presented lower temporal standard deviation (0.18 ± 0.06 Hz vs. 0.29 ± 0.12 Hz, p < 0.05) and higher OI (0.35 ± 0.03 vs. 0.31 ± 0.04, p < 0.05). The atrial regions with the highest proportion of HDF region were the septum and the left upper pulmonary vein. Conclusion: Multiple recurrent spatiotemporal HDF patterns exist during persAF. The proposed method can identify and quantify the spatiotemporal repetition of the HDFs, where the high recurrences of DP may suggest a more organized rhythm. DPs presented a more consistent DF and higher organization compared with non-DPs, suggesting that DF with higher OI might be more likely to recur. Recurring patterns offer a more comprehensive dynamic insight of persAF behavior, and ablation targeting such regions may be beneficial.

Dataset Information

Time- and frequency-domain analyses of atrial fibrillation activation rate: the optical mapping reference.

Publications

Time- and frequency-domain analyses of atrial fibrillation activation rate: the optical mapping reference.

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