Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots

Zhang, Lijuan; Guo, Tiantian; Xi, Bin; Yang, Fan; Wang, Kun; Bi, Jiacheng; Wang, Ying

doi:10.1088/0967-3334/36/2/283

Cited by 32 publications

(17 citation statements)

References 24 publications

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“…Arising from the normal sinus node the relationship of each beat to that of the next is dependent on the intrinsic characteristics of the pacing cells (Gao et al, 2010;Nicolini et al, 2012;Monfredi et al, 2013;Yaniv et al, 2014a,b;Valente et al, 2018) and the external forces of the autonomic nervous system (Billman, 2011). Recent studies reveal that the stochastic (unpredictable) (Yaniv et al, 2014b) and chaotic (deterministic) (Nicolini et al, 2012;Yaniv et al, 2014b;Zhang et al, 2015) mechanisms that regulate the beating heart are caused by the complexity of structure (Fedorov et al, 2009;Ambrosi et al, 2012) and function of the sinus node in both the dog and human (Fedorov et al, 2009(Fedorov et al, , 2010(Fedorov et al, , 2012Glukhov et al, 2013;Csepe et al, 2016;Kalyanasundaram et al, 2019). The sinus node is not simply a cluster of the most rapidly depolarizing cells of the heart.…”

Section: Introductionmentioning

confidence: 99%

“…Time, (Malik et al, 1996;Shaffer and Ginsberg, 2017) frequency, (Csepe et al, 2016) and geometric (tachograms, histograms, Poincaré plots) (Esperer et al, 2008;Khandoker et al, 2013;Borracci et al, 2018) domain indices are often used in the assessment of heart rate (sinus) variability (Malik et al, 1996;Billman, 2011;Billman et al, 2015b;Shaffer and Ginsberg, 2017). Furthermore, to understand better the complexity of biophysical oscillators like the sinus node, advanced methods have been developed to analyze heart rate variability such as measures of fractal-like behavior (long-term analysis), detrended fluctuation (Stein et al, 2005) (short-term analysis), disorder (approximate and sample entropy), non-linear dynamical systems (multiscale Poincaré plots) (Henriques et al, 2015;Shaffer and Ginsberg, 2017) and chaotic behavior (Nicolini et al, 2012;Sassi et al, 2015;Zhang et al, 2015;Behar et al, 2018b;Valente et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Beat-to-Beat Patterning of Sinus Rhythm Reveals Non-linear Rhythm in the Dog Compared to the Human

2020

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The human and dog have sinus arrhythmia; however, the beat-to-beat interval changes were hypothesized to be different. Geometric analyses (R-R interval tachograms, dynamic Poincaré plots) to examine rate changes on a beat-to-beat basis were analyzed along with time and frequency domain heart rate variability from 40 human and 130 canine 24-h electrocardiographic recordings. Humans had bell-shaped beat-interval distributions, narrow interval bands across time with continuous interval change and linear changes in rate. In contrast, dogs had skewed non-singular beat distributions, wide interval bands {despite faster average heart rate of dogs [mean (range); 81 (64-119)] bpm compared to humans [74.5 (59-103) p = 0.005]} with regions displaying a paucity of intervals (zone of avoidance) and linear plus non-linear rate changes. In the dog, dynamic Poincaré plots showed linear rate changes as intervals prolonged until a point of divergence from the line of identity at a mean interval of 598.5 (95% CI: 583.5-613.5) ms (bifurcation interval). The dog had bimodal beat distribution during sleep with slower rates and greater variability than during active hours that showed singular interval distributions, higher rates and less variability. During sleep, Poincaré plots of the dog had clustered or branched patterns of intervals. A slower rate supported greater parasympathetic modulation with a branched compared to the clustered distribution. Treatment with atropine eliminated the non-linear patterns, while hydromorphone shifted the bifurcated branching and beat clustering to longer intervals. These results demonstrate the unique non-linear nature of beat-to-beat variability in the dog compared to humans with increases in interval duration (decrease heart rate). These results provoke the possibility that changes are linear with a dominant sympathetic modulation and non-linear with a dominant parasympathetic modulation. The abrupt bifurcation, zone of avoidance and beat-to-beat patterning are concordant with other studies demonstrating the development of exit block from the sinus node with parasympathetic modulation influencing not only the oscillation of the pacing cells, but conduction to the atria. Studies are required to associate the in vivo sinus node beat patterns identified in this study to the mapping of sinus impulse origin and exit from the sinus node.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Beat-to-Beat Patterning of Sinus Rhythm Reveals Non-linear Rhythm in the Dog Compared to the Human

2020

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“…We plotted histograms of the VV values and Poincaré plots of consecutive VV values, in order to visually assess potential differences between NA and A groups, and differences between the AF and PVC groups within the A patient group [26, 27]. Poincaré plots have been previously used to analyze heart-rate variability [28], plotting each RR interval as a function of the preceding RR interval.…”

Section: Methodsmentioning

confidence: 99%

Use of self-gated radial cardiovascular magnetic resonance to detect and classify arrhythmias (atrial fibrillation and premature ventricular contraction)

Piekarski

Chițiboi

Ramb

et al. 2016

Journal of Cardiovascular Magnetic Resonance

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BackgroundArrhythmia can significantly alter the image quality of cardiovascular magnetic resonance (CMR); automatic detection and sorting of the most frequent types of arrhythmias during the CMR acquisition could potentially improve image quality. New CMR techniques, such as non-Cartesian CMR, can allow self-gating: from cardiac motion-related signal changes, we can detect cardiac cycles without an electrocardiogram. We can further use this data to obtain a surrogate for RR intervals (valley intervals: VV). Our purpose was to evaluate the feasibility of an automated method for classification of non-arrhythmic (NA) (regular cycles) and arrhythmic patients (A) (irregular cycles), and for sorting of common arrhythmia patterns between atrial fibrillation (AF) and premature ventricular contraction (PVC), using the cardiac motion-related signal obtained during self-gated free-breathing radial cardiac cine CMR with compressed sensing reconstruction (XD-GRASP).MethodsOne hundred eleven patients underwent cardiac XD-GRASP CMR between October 2015 and February 2016; 33 were included for retrospective analysis with the proposed method (6 AF, 8 PVC, 19 NA; by recent ECG). We analyzed the VV, using pooled statistics (histograms) and sequential analysis (Poincaré plots), including the median (medVV), the weighted mean (meanVV), the total number of VV values (VVval), and the total range (VVTR) and half range (VVHR) of the cumulative frequency distribution of VV, including the median to half range (medVV/VVHR) and the half range to total range (VVHR/VVTR) ratios. We designed a simple algorithm for using the VV results to differentiate A from NA, and AF from PVC.ResultsBetween NA and A, meanVV, VVval, VVTR, VVHR, medVV/VVHR and VVHR/VVTR ratios were significantly different (p values = 0.00014, 0.0027, 0.000028, 5×10−9, 0.002, respectively). Between AF and PVC, meanVV, VVval and medVV/VVHR ratio were significantly different (p values = 0.018, 0.007, 0.044, respectively). Using our algorithm, sensitivity, specificity, and accuracy were 93 %, 95 % and 94 % to discriminate between NA and A, and 83 %, 71 %, and 77 % to discriminate between AF and PVC, respectively; areas under the ROC curve were 0.93 and 0.89.ConclusionsOur study shows we can reliably detect arrhythmias and differentiate AF from PVC, using self-gated cardiac cine XD-GRASP CMR.

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“…Poincaré plot analysis, describing each RR interval versus the previous one, has been widely used to classify different rhythms. Previous studies were based on the definition of parameters and thresholds describing particular patterns in the plot [4][5][6][7][8][9]. However, some patterns either cannot be easily described by simple parameters or cannot be intuitively appreciated.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Tachyarrhythmia Detection by Poincaré Plot-Based Image Analysis

Isla¹,

Corino²,

Mainardi³

2019

2019 Computing in Cardiology Conference (CinC)

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Tachyarrhythmia detection through RR interval analysis could improve performance of monitoring devices. In this paper a Poincaré plot-based image approach is presented. Three cardiac rhythms were analyzed in this study: normal sinus rhythm (NSR), atrial fibrillation (AF) and atrial bigeminy (AB). Using different MIT-BIH databases, 27955, 3363 and 76 images were generated for NSR, AF and AB respectively using a 2-minute window with 50 % overlap. The 80 % of the data available for each rhythm was used to create a reference rhythm image atlas. The remaining 20 % was classified into one of the three categories using mutual information. The process was iterated 10 times, in which images used to construct the atlas and used to create the test set were randomly selected. AF was correctly classified 94.12 %±0.45, AB 72.00 %±11.24 and NSR 80.70 %±0.54. The results of the present study suggest that Poincaré plot-based image analysis is a promising path for classifying different rhythms using only ventricular activity.

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Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots

Cited by 32 publications

References 24 publications

Beat-to-Beat Patterning of Sinus Rhythm Reveals Non-linear Rhythm in the Dog Compared to the Human

Beat-to-Beat Patterning of Sinus Rhythm Reveals Non-linear Rhythm in the Dog Compared to the Human

Use of self-gated radial cardiovascular magnetic resonance to detect and classify arrhythmias (atrial fibrillation and premature ventricular contraction)

Cardiac Tachyarrhythmia Detection by Poincaré Plot-Based Image Analysis

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