Background: Atrial fibrillation (AF) ablation guided by complex fractionated electrograms (CFE) has been described, but the spatial and temporal stability of the electrograms (EGMs) has been questioned.
Objective: The purpose of this study was to prospectively assess the spatial and temporal stability of CFE in patients with persistent AF.
Methods: Twenty-four patients were studied. For 12 patients, two high-density CFE maps were performed during AF at baseline (0 minute) and 20 minutes later using the EnSite NavX system. Six-second bipolar EGMs were collected throughout the left atrium (LA) using a circular mapping catheter. Automated software measured the time between discrete local EGM deflections yielding a mean local cycle length (CL). EGMs with mean CL <120 ms were considered CFE. The LA was divided into six regions. Spatial distribution of EGMs at 0 and 20 minutes was compared in each region across three different CL ranges (A = 50-120 ms, B = 121-200 ms, C = 200-500 ms). The 0- and 20-minute CFE maps were directly superimposed offline in MATLAB, and the mean CL value for each point that was sampled in both maps was compared in each CL range (A-C). For the other 12 patients, repetitive measurements (1-minute intervals for 5 minutes) of mean CL were obtained at a sample point for each CL range (A-C) in each patient and compared for consistency.
Results: A total of 324 +/- 65 points were collected per map. Globally in the LA, the distribution of CLs did not change from 0 to 20 minutes (A: 47.1% vs 45.0%; B: 35.3% vs 36.5%; C: 16.0% vs 16.9%; P = .6). The CL distribution in each of the six regions of the LA also did not change significantly from 0 to 20 minutes. There was no significant change in repetitive CL measures for range A (mean DeltaCL 12 +/- 4 ms, P = .4). However, there was significant variation over 5 minutes for ranges B and C (mean DeltaCL 39 +/- 19 ms and 48 +/- 22 ms, respectively, P <.05 for both). Superimposing maps showed 74.7% point-to-point match for range A, 39.3% for range B, and 14.2% for range C (within 30 ms), with a significant correlation seen only for range A (r = 0.82, P <.001).
Conclusion: CFE show a high degree of spatial and temporal stability. Greater temporal variation is seen in progressively longer CL regions that are outside of the CFE region of interest.
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http://dx.doi.org/10.1016/j.hrthm.2008.04.027 | DOI Listing |
Health Sci Rep
January 2025
Department of Environment, Development and Sustainability Studies, School of Natural Sciences, Environment and Technology Södertörn University Huddinge Stockholm Sweden.
Geosci Model Dev
November 2024
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
United States (US) background ozone (O) is the counterfactual O that would exist with zero US anthropogenic emissions. Estimates of US background O typically come from chemical transport models (CTMs), but different models vary in their estimates of both background and total O. Here, a measurement-model data fusion approach is used to estimate CTM biases in US anthropogenic O and multiple US background O sources, including natural emissions, long-range international emissions, short-range international emissions from Canada and Mexico, and stratospheric O.
View Article and Find Full Text PDFThe hippocampus forms memories of our experiences by registering processed sensory information in coactive populations of excitatory principal cells or ensembles. Fast-spiking parvalbumin-expressing inhibitory neurons (PV INs) in the dentate gyrus (DG)-CA3/CA2 circuit contribute to memory encoding by exerting precise temporal control of excitatory principal cell activity through mossy fiber-dependent feed-forward inhibition. PV INs respond to input-specific information by coordinating changes in their intrinsic excitability, input-output synaptic-connectivity, synaptic-physiology and synaptic-plasticity, referred to here as experience-dependent PV IN plasticity, to influence hippocampal functions.
View Article and Find Full Text PDFPurpose: To propose a domain-conditioned and temporal-guided diffusion modeling method, termed dynamic Diffusion Modeling (dDiMo), for accelerated dynamic MRI reconstruction, enabling diffusion process to characterize spatiotemporal information for time-resolved multi-coil Cartesian and non-Cartesian data.
Methods: The dDiMo framework integrates temporal information from time-resolved dimensions, allowing for the concurrent capture of intra-frame spatial features and inter-frame temporal dynamics in diffusion modeling. It employs additional spatiotemporal ($x$-$t$) and self-consistent frequency-temporal ($k$-$t$) priors to guide the diffusion process.
Clin EEG Neurosci
January 2025
Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
Motor Imagery (MI) electroencephalographic (EEG) signal classification is a pioneer research branch essential for mobility rehabilitation. This paper proposes an end-to-end hybrid deep network "Spatio Temporal Inception Transformer Network (STIT-Net)" model for MI classification. Discrete Wavelet Transform (DWT) is used to derive the alpha (8-13) Hz and beta (13-30) Hz EEG sub bands which are dominant during motor tasks to enhance the performance of the proposed work.
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