Intervals Of Activity Research Articles

Effects of refractory gradients and ablation on fibrillatory activity

BackgroundThe mechanisms involved in onset, maintenance, and termination of atrial fibrillation are not well understood. A biophysical model could be useful to determine how the events unfold. MethodA two-dimensional cellular automaton consisting of 576 × 576 grid nodes was implemented to demonstrate the types of electrical activity that may occur in compromised atrial substrate. Electrical activation between nodes was made anisotropic (2:1), and the refractory period (RP) was adjusted from 74 to 192 ms in the spatial domain. Presence of collagen fibers were simulated as short lines of conduction block at many random grid sites, while ablation lesions were delineated as longer lines of block. An S1-S2 pulse from one grid corner was utilized to initiate simulated electrical activity. Simulations were done in which 1. no ablation lines, 2. random ablation lines, and 3. parallel ablation lines were added to the grid to determine how this affected the formation and annihilation of rotational activity after S1-S2 stimulation. ResultsAs the premature (S2) wavefront traversed the grid, rotational activity formed near boundaries where wavefronts propagated from shorter to longer refractory regions, causing unidirectional block, and were anchored by fiber clusters. Multiple wavelets appeared when wavefronts originating from different driving rotational features collided, and/or by their encounter with RP discontinuities. With the addition of randomly orientated simulated ablation lesions, followed by reinduction of fibrillatory activity, mean activation interval (AI) prolonged from a baseline level of 144.2 ms–160.3 ms (p < 0.001 in most comparisons). During fibrillatory activity, when parallel ablation lines were added to short RP regions, AI prolonged to 150.4 ms (p < 0.001), and when added to long RP regions, AI prolonged to 185.3 ms (p < 0.001). In all cases, AI prolongation after simulated ablation resulted from reduced number and/or from the isolation of local drivers, so that distant drivers in short RP regions activated long RP regions N:1, while distant drivers in long RP regions activated short RP regions at a relatively slow rate. ConclusionsAn automaton model was found useful to generate and test hypotheses concerning fibrillatory activity, which can then be validated in the clinical electrophysiology laboratory.

Bipolar Intracardiac Electrogram Active Interval Extraction During Atrial Fibrillation.

We introduce novel methods to identify the active intervals (AIs) of intracardiac electrograms (IEGMs) during complex arrhythmias, such as atrial fibrillation (AF). We formulate the AI extraction problem, which consists of estimating the beginning and duration of the AIs, as a sequence of hypothesis tests. In each test, we compare the variance of a small portion of the bipolar IEGM with its adjacent segments. We propose modified general-likelihood ratio (MGLR) and separating-function-estimation tests; we derive five test statistics (TSs), and show that the AIs can be obtained by threshold crossing the TSs. We apply the proposed methods to the IEGM segments collected from the left atrium of 16 patients (62.4 ± 8.2-years old, four females, four paroxysmal, and twelve persistent AF) prior to catheter ablation. The accuracy of our methods is evaluated by comparing them with previously developed methods and manual annotation (MA). Our results show a high level of similarity between the AIs of the proposed methods and MA, e.g., the true and false positive rates of one of the MGLR-based methods were, respectively, 97.8% and 1.4%. The mean absolute error from estimation of the onset and end of AIs and also for the estimation of the mean cycle length for that approach was 8.7 ± 10.5, 13 ± 15.5, and 4.2 ± 9.4 ms, respectively. The proposed methods can accurately identify onset and duration of AI of the IEGM during AF. The proposed methods can be used for real-time automated analysis of AF, the most challenging complex arrhythmia.

Bursts of Node Activation and Asynchronous Communication in Temporal Networks

Development of sensor technologies and the prevalence of electronic communication services provide us with a huge amount of data on human communication behavior, including face-to-face conversations, e-mail exchanges, phone calls, message exchanges and other types of interactions in various online forums. These indirect or direct interaction form potential bridges of the virus spread. For a long time, the study of virus spread is based on the aggregate static network. However, the interaction patterns containing diverse temporal properties may affect dynamic processes as much as the network topology does. Some empirical studies show, the activation time and duration of vertices and links are highly heterogeneous, which means intense activity may be followed by longer intervals inactivity. We take heterogeneous distribution of the node inter-activation time as the research background to build an asynchronous communication model. The two sides of the communication don't have to be active at the same time. One derives the threshold of virus spreading on the communication mode and analyzes the reason the heterogeneous distribution of the vertex inter-activation time suppress the spread of virus. At last, the analysis and results from the model are verified on the BA network.

Sunspot Time Series: Passive and Active Intervals

Solar activity slowly and irregularly decreases from the first spotless day (FSD) in the declining phase of the old sunspot cycle and systematically, but also in an irregular way, increases to the new cycle maximum after the last spotless day (LSD). The time interval between the first and the last spotless day can be called the passive interval (PI), while the time interval from the last spotless day to the first one after the new cycle maximum is the related active interval (AI). Minima of solar cycles are inside PIs, while maxima are inside AIs. In this article, we study the properties of passive and active intervals to determine the relation between them. We have found that some properties of PIs, and related AIs, differ significantly between two group of solar cycles; this has allowed us to classify Cycles 8 – 15 as passive cycles, and Cycles 17 – 23 as active ones. We conclude that the solar activity in the PI declining phase (a descending phase of the previous cycle) determines the strength of the approaching maximum in the case of active cycles, while the activity of the PI rising phase (a phase of the ongoing cycle early growth) determines the strength of passive cycles. This can have implications for solar dynamo models. Our approach indicates the important role of solar activity during the declining and the rising phases of the solar-cycle minimum.

Sympathetic nerve stimulation produces spatial heterogeneities of action potential restitution

Restitution kinetics (RK) of action potential duration (APD) are classically studied by applying an extra impulse at varying diastolic intervals (DI) and might differ from RK elicited by sympathetic nerve stimulation (SNRK). To measure 'Physiological' RK during gradual increases in heart rate caused by sympathetic nerve stimulation (SNS) and its possible spatial heterogeneities caused by non-uniform innervation of the myocardium. The SNRK was measured from rabbit hearts with intact sympathetic innervation using optical mapping. APD versus DI were plotted from left ventricles during SNS, then with pacing using identical activation interval (AI) sequences. AI decreased (444 +/- 18 ms to 284 +/- 9 ms) in 25 s and recovered to baseline on SNS suspension (n = 10). APD versus DI plots were identical for SNS and pacing except that when maximum heart rate was reached, SNS elicited a further APD shortening compared with pacing. During SNS, APDs decreased from 216 +/- 9 ms to 154 +/- 7 ms (29% +/- 2%) at the base and from 206 +/- 12 ms to 158 +/- 7 ms (20% +/- 2%) at the apex. During pacing, APDs at the base decreased from 216 +/- 9 ms to 170 +/- 7 ms (21% +/- 1%, SNS versus pacing: P < .05). In contrast, RK were similar at the apex for SNS and pacing. During SNS, the extra APD shortening at the base was associated with longer segments of RK curves with negative slopes and extra DI prolongation. Perfusion with the I(Ks) inhibitor HMR 1556 (0.5 microM; n = 5) abolished differences in RK between SNS and pacing. Levels of KCNQ1 and tyrosine hydroxylase proteins were greater at the base than apex (n = 4), implying that apex-base distributions for I(Ks) and sympathetic innervation are congruent with SNRK heterogeneities. The findings shed further insights on heterogeneities of sympathetic nerves, RK, and the role of I(Ks) in cardiac repolarization.

Action potential duration restitution during sympathetic nerve stimulation versus pacing

Cardiac restitution kinetics (RK) of action potential duration (APD) has been extensively linked to arrhythmia vulnerability yet because of technical limitations, RK has not been measured when diastolic intervals (DI) are altered by direct sympathetic nerve activity. We used a novel isolated, fully innervated perfused rabbit heart preparation and combined it with optical mapping to measure APD RK in the left ventricle (n=3). Sympathetic nerves were stimulated bilaterally at the thoracic outflow in the spinal cord at 5, 10 or 15 Hz until heart rate (HR) reached a maximum and APDs a minimum then nerve stimulation was stopped and HR allowed to recover while tracking beat-to-beat activation intervals (AI) and APDs.

The distribution of refractory periods influences the dynamics of ventricular fibrillation.

The spatial and dynamic properties of ventricular fibrillation (VF) may be random or related to cellular electrical properties of the normal heart. Local activation intervals (AIs) in VF may depend on the local refractory period (RP), and sustained VF may require a steep action potential (AP) restitution curve. In guinea pig hearts, AP durations (APDs) and RPs on the epicardium are shorter at the apex and progressively longer toward the base, producing gradients of RPs that may influence the spatial organization of VF. In the present study, the influence of APDs on VF dynamics is investigated in perfused guinea pig hearts stained with a voltage-sensitive dye by comparing APD gradients to the dynamics of VF elicited by burst pacing. In VF, AIs had no clear periodicity, but average AIs were shorter at the apex (57.5+/-8.1 ms) than the base (76.1+/-1.5 ms, n=6, P<0.05) and had gradients similar to APD gradients (correlation coefficient 0.71+/-0.04). Analysis of local velocity vectors showed no preferential directions, and fast Fourier transform (FFT) power spectra were broad (10 to 24 Hz) with multiple peaks (n=6). However, the selective inhibition of delayed K(+) rectifying currents, I(Kr) (E4031; 0.5 micromol/L, n=3), shifted FFT spectra from complex to a lower dominant frequency (10 Hz) and altered repolarization but retained the correlation between mean AIs and RPs. Thus, VF dynamics are consistent with a multiple wave-make and wave-break mechanism, and the local RP influences VF dynamics by limiting the range of VF frequencies and AIs at each site. The full text of this article is available at http://www.circresaha.org.