Circadian Modulation of Atrial Cycle Length in Human Chronic Permanent Atrial Fibrillation: A Noninvasive Assessment Using Long-Term Surface ECG

Heart rate characteristic based modelling of atrial fibrillatory rate using implanted cardiac monitor data

Objective. The objective of the present study is to investigate the feasibility of using heart rate characteristics to estimate atrial fibrillatory rate (AFR) in a cohort of atrial fibrillation (AF) patients continuously monitored with an implantable cardiac monitor. We will use a mixed model approach to investigate population effect and patient specific effects of heart rate characteristics on AFR, and will correct for the effect of previous ablations, episode duration, and onset date and time.Approach. The f-wave signals, from which AFR is estimated, were extracted using a QRST cancellation process of the AF episodes in a cohort of 99 patients (67% male; 57 ± 12 years) monitored for 9.2(0.2-24.3) months as median(min-max). The AFR from 2453 f-wave signals included in the analysis was estimated using a model-based approach. The association between AFR and heart rate characteristics, prior ablations, and episode-related features were modelled using fixed-effect and mixed-effect modelling approaches.Main results. The mixed-effect models had a better fit to the data than fixed-effect models showing h.c. of determination (R2 = 0.49 versusR2 = 0.04) when relating the variations of AFR to the heart rate features. However, when correcting for the other factors, the mixed-effect model showed the best fit (R2 = 0.04). AFR was found to be significantly affected by previous catheter ablations (p< 0.05), episode duration (p< 0.05), and irregularity of theRRinterval series (p< 0.05).Significance. Mixed-effect models are more suitable for AFR modelling. AFR was shown to be faster in episodes with longer duration, less organizedRRintervals and after several ablation procedures.

Analysis of surface atrial signals: time series with missing data?

Uncovering of the atrial signal for patients undergoing episodes of atrial fibrillation is usually obtained from surface ECG by removing waves induced by ventricular activities. Once earned the atrial signal, the detection of the dominant fibrillation frequency is often the main (and only) goal. In this work we verified if subtraction of the ventricular activity might be avoided by performing spectral analysis on those ECG segments where ventricular activity is absent, (i.e. the T-Q intervals). While the approach might seem crude, in here the question was recast into a problem of missing data in a long time series and proper methods were applied: the Lomb periodogram and the iterative Singular Spectrum Analysis. The two methods were tested on both simulated signals and "realistic" atrial signals constructed using the ECG recordings provided by the 2004 Computers in Cardiology competition. The results obtained showed that both techniques were able to provide a reliable quantification of the dominant oscillation, with a slightly superior performance of the iterative Singular Spectrum Analysis. Absolute errors larger than 1.0 Hz were unlikely (p < 0.05) up to 130-140 bpm. Such level of agreement is consistent with similar comparative works where techniques for separating the atrial signal from ventricular waves were considered.