UNSTABLE CARDIAC SINGULARITIES MAY LEAD TO ATRIAL FIBRILLATION

2011 ◽  
Vol 21 (04) ◽  
pp. 1141-1151 ◽  
Author(s):  
C. L. WEBBER ◽  
Z. HU ◽  
J. G. AKAR
Keyword(s):  
Atrial Fibrillation ◽  
Full Range ◽  
Central Line ◽  
Recurrence Quantification Analysis ◽  
Spiral Waves ◽  
Atrial Arrhythmias ◽  
Recurrence Plots ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Derivatives Of

A total of 53 atrial electrograms were recorded from 12 human patients diagnosed with different degrees of atrial arrhythmias and fibrillation, but not atrial flutter. The atrial waves were highly complex, noisy, nonuniform, nonlinear, and nonstationary in time and well suited for recurrence quantification analysis (RQA), spectral analysis (FFT) and atrial rate (AR) measurements. Differing degrees of atrial arrhythmias were quantified by measuring singularities in the electrograms. Singularities were defined as the maximum periods of relative isopotential squared (msec2) and presented as unfilled squares along the central line of identity (LOI) on recurrence plots. These nonsolid (unfilled) squares indicate that most singularities were unstable with noisy baselines. All measured variables were plotted against their corresponding unstable singularities. The best correlations were found for variables Vmaxand Laminar over the full range of log10(singularity). That is, the higher the degree of fibrillation the smaller the size of the singularity and the shorter Vmaxand Laminar. The shorter singularities are associated with faster spiral waves. However, since Vmaxand Laminar are direct derivatives of Singularity, this variable remains the sole best quantifier of choice to identify aberrant pacemaker regions.

Abstract 18637: Recurrence Quantification Analysis to Distinguish Active From Passive Mechanisms of CFAEs During Catheter Ablation of Atrial Fibrillation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Alex Baher ◽  
Anil K Gehi ◽  
Prabhat Kumar ◽  
Eugene Chung ◽  
Benjamin H Buck ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Recurrence Quantification Analysis ◽  
Slow Conduction ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Mean Cycle ◽  
Recurrent Patterns ◽  
Wave Break ◽  
Complex Fractionated Atrial Electrograms

Background: Ablation of complex fractionated atrial electrograms (CFAEs) is controversial, primarily because of difficulty in visually distinguishing CFAEs representing an active site of driver activity from a passive site of double potentials, wave break, and/or slow conduction. We hypothesized that CFAEs within rotors in atrial fibrillation (AF) would exhibit highly recurrent behavior compared with CFAEs remote from these driver regions. Methods: Active and passive mechanisms of CFAE formation were simulated in several 2D 7.5 x 7.5 cm modified Luo-Rudy 1 models. CFAEs within areas of rotors were considered active, while those caused by wave break, slow conduction or double potentials remote from rotors were considered passive. Clinical signals were also collected during catheter ablation of paroxysmal AF (n=8 patients). An active driver CFAE site was defined by termination of AF with ablation followed by non-inducibility. A passive site was defined as CFAE occurring remotely. Detection of CFAEs was based on mean cycle length (MCL) calculated from 4 second windows using -dV/dt for detection (40ms refractory period/10ms maximum EGM width for simulations; 45ms/15ms respectively for clinical signals). Recurrence quantification analysis (RQA) was performed on discrete time series of simulated and clinical CFAE activations. Results: RQA was performed on 20 simulated EGMs. MCL was similar in both active and passive CFAEs (74±11ms and 78±6ms respectively, p=NS), but recurrence was significantly higher in active compared to passive sites (%recurrence: 61±22% active, 4±6% passive, p<0.01). In patients with AF, the driver sites were all located within the pulmonary vein antra while passive CFAEs were remote. The MCL of CFAEs at active driver sites was similar to that of passive sites (100±13ms active, 98±17ms passive, p=NS), but recurrence was significantly higher in the active driver sites (%recurrence: 18±15% active, 2±1% passive, p=0.02). Conclusion: CFAEs may occur due to either active or passive mechanisms. Sites within rotors or focal drivers of AF are more likely to exhibit recurrent patterns. RQA may be a powerful tool to differentiate driver from bystander CFAEs enabling more efficient targeting for ablation.

RECURRENCE QUANTIFICATION ANALYSIS OF ELECTROOCULOGRAPHY SIGNAL TO A CONTROL QUESTION TEST: A NEW APPROACH FOR THE DETECTION OF DECEPTION

2020 ◽  
Vol 32 (04) ◽  
pp. 2050029
Author(s):  
Mohamad Amin Younessi Heravi ◽  
Morteza Pishghadam ◽  
Hosnieh Raoufian ◽  
Akram Gazerani
Keyword(s):  
Detection System ◽  
Lie Detection ◽  
Recurrence Quantification Analysis ◽  
Recurrence Plots ◽  
New Approach ◽  
Control Question ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
Detection Of Deception ◽  
One Way Anova

This study aimed to evaluate a lie-detection system by nonlinear analysis of electrooculography (EOG) signals in the polygraph test. The physiological signals such as photoplethysmography signal, electrodermal response, respiratory changes as well as EOG signal were recorded based on a Control Question Test (CQT). Three psychophysiological signals were evaluated based on the extracted features in the seven-position numerical scoring. The dynamics of EOG signals in subjects that had a total negative score were analyzed by recurrence quantification analysis (RQA). The six values of RQA were calculated to analyze the EOG signals in relevant questions compared to other questions. A one-way ANOVA with multiple comparisons was performed to evaluate the extracted variables in different questions. Eleven subjects had a total score of [Formula: see text]2 and less, so the EOG signals of these subjects were evaluated. Recurrence plots (RPs) of EOG signals showed clear differences in the two types of questions. The recurrence quantification analysis of vertical EOG signal indicated that [Formula: see text] and determinism (DET) values decreased significantly for relevant questions compared to other questions. Moreover, a significant decrease was observed in all RQA parameters except RR for the horizontal EOG signal. The differences of EOG signals in relevant questions observed using RPs and RQA were possibly related to the underlying changes in rapid eye movement due to the stress. The results of this study illustrate that the RQA technique is well suited to analyze the EOG signals in the detection of deception.

Recurrence Plots for Diesel Engine Variability Tests

2009 ◽  
Vol 64 (1-2) ◽  
pp. 96-102 ◽  
Author(s):  
Rafał Longwic ◽  
Grzegorz Litak ◽  
Asok K. Sen
Keyword(s):  
Diesel Engine ◽  
Wavelet Analysis ◽  
Low Frequency ◽  
Recurrence Quantification Analysis ◽  
Maximum Pressure ◽  
Recurrence Plots ◽  
Frequency Bands ◽  
Recurrence Quantification ◽  
Quantification Analysis

Cycle-to-cycle variations of maximum pressure in a diesel engine are studied by using the methods of recurrence plots and recurrence quantification analysis. The pressure variations are found to exhibit strong periodicities in low frequency bands and intermittent oscillations at higher frequencies. The results are confirmed by wavelet analysis.

Recurrence Plot and Recurrence Quantification of the Dynamic Properties of Cross-Shaped Laminated Energy Harvester

2016 ◽  
Vol 849 ◽  
pp. 95-105 ◽  
Author(s):  
Peter Harris ◽  
Grzegorz Litak ◽  
Joanna Iwaniec ◽  
Chris R. Bowen
Keyword(s):  
Power Output ◽  
Dynamic Properties ◽  
Electrical Power ◽  
Mechanical Vibration ◽  
Recurrence Plot ◽  
Recurrence Quantification Analysis ◽  
Recurrence Plots ◽  
Recurrence Quantification ◽  
Time Histories ◽  
Quantification Analysis

The paper examines the dynamic properties of bistable cross-shaped laminate plates for broadband energy harvesting applications by converting mechanical vibration energy into the electrical power output. Bistable laminates plates coupled to piezoelectric transducers were excited by application of harmonic excitations and exhibited a range of vibration patterns. The vibration patterns included single-well oscillations and snap-through vibrations of both periodic and chaotic character; such vibration patterns led to a different power output. Classical spectral analysis of measured voltage, displacement and velocity time histories indicated the presence of a variety of nonlinear and chaotic phenomena. As a result, an analysis of the measured displacement and voltage time histories was carried out with the use of the Recurrence Plots and the Recurrence Quantification Analysis methods. The Recurrence Plots method was used for detection of qualitative changes in the dynamic behaviour of the non-linear harvesting system. In order to facilitate interpretation of piezoelectric voltage and laminate displacement, a detailed analysis using Recurrence Plots, Recurrence Quantification Analysis was employed.

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