Geometric patterns of time‐delay plots from different cardiac rhythms and arrhythmias using short‐term <scp>EKG</scp> signals

Borracci, Raúl A.; Pulvet, José D. Montoya; Ingino, Carlos A.; Maurice, Mario Fitz; Prado, Alfredo Hirschon; Dominé, Enrique

doi:10.1111/cpf.12494

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…Moreover, linear quantification are inappropriate, thus non-linear analyses demand exploration. These may include approximate or sample entropy, detrended fluctuation analyses and fractal measures, as well as the development of new methodologies through computer modeling of the beat-to-beat variation (Esperer et al, 2008;Nicolini et al, 2012;Khandoker et al, 2013;Burykin et al, 2014;Yaniv et al, 2014b;Henriques et al, 2015;Shaffer and Ginsberg, 2017;Borracci et al, 2018;Valente et al, 2018). In humans, slowing of the heart rate is a continuum with some having slower rates (e.g., athletes), but in the normal dog slowing of the heart is not a continuous process.…”

Section: Beyond Visual Geometric Analysesmentioning

confidence: 99%

“…Long-term electrocardiographic (Holter) recordings have permitted the appraisal of rhythm during variation of autonomic input throughout the day. 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: Beyond Visual Geometric Analysesmentioning

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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“…The dogs included in this study were those hypothesized to have sinoatrial conduction block based on the ECGs, tachograms and Poincaré plots 4‐6,24,28,29,53‐55 . Notably, in the diseased sinus node, multiple overlapping mechanisms are possible for failed impulse generation and conduction.…”

Section: Discussionmentioning

confidence: 99%

“…The dogs included in this study were those hypothesized to have sinoatrial conduction block based on the ECGs, tachograms and Poincaré plots. [4][5][6]24,28,29,[53][54][55] Notably, in the diseased sinus node, multiple overlapping mechanisms are possible for failed impulse generation and conduction. The anatomy and function of the sinus node is complex with profound influence on rate and rhythm from the autonomic nervous systems.…”

Section: Discussionmentioning

confidence: 99%

Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs

Flanders,

Moïse,

Otani

2024

Veterinary Internal Medicne

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BackgroundSinus node dysfunction because of abnormal impulse generation or sinoatrial conduction block causes bradycardia that can be difficult to differentiate from high parasympathetic/low sympathetic modulation (HP/LSM).HypothesisBeat‐to‐beat relationships of sinus node dysfunction are quantifiably distinguishable by Poincaré plots, machine learning, and 3‐dimensional density grid analysis. Moreover, computer modeling establishes sinoatrial conduction block as a mechanism.AnimalsThree groups of dogs were studied with a diagnosis of: (1) balanced autonomic modulation (n = 26), (2) HP/LSM (n = 26), and (3) sinus node dysfunction (n = 21).MethodsHeart rate parameters and Poincaré plot data were determined [median (25%‐75%)]. Recordings were randomly assigned to training or testing. Supervised machine learning of the training data was evaluated with the testing data. The computer model included impulse rate, exit block probability, and HP/LSM.ResultsConfusion matrices illustrated the effectiveness in diagnosing by both machine learning and Poincaré density grid. Sinus pauses >2 s differentiated (P < .0001) HP/LSM (2340; 583‐3947 s) from sinus node dysfunction (8503; 7078‐10 050 s), but average heart rate did not. The shortest linear intervals were longer with sinus node dysfunction (315; 278‐323 ms) vs HP/LSM (260; 251‐292 ms; P = .008), but the longest linear intervals were shorter with sinus node dysfunction (620; 565‐698 ms) vs HP/LSM (843; 799‐888 ms; P < .0001).ConclusionsNumber and duration of pauses, not heart rate, differentiated sinus node dysfunction from HP/LSM. Machine learning and Poincaré density grid can accurately identify sinus node dysfunction. Computer modeling supports sinoatrial conduction block as a mechanism of sinus node dysfunction.

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“…Te plot has the function to graphically reveal the physiological status of the cardiovascular system. Based on Poincaré plot, HRV can be efectively analyzed and various cardiovascular diseases display distinct distribution patterns [20][21][22], for instance, the heart rate of a healthy human appears "comet-shape," the patient with serious heart failure is "fan-shaped," and the heart rate produced by the artifcial pacemaker is "round." For describing these distribution patterns of scatter points quantitatively, many analysis algorithms were presented, such as the minor and major axis of the ftted ellipse (SD1, SD2) [23], the ftted ellipse area of scatter points (S) [24], Ehler index (EI) [25], Guzik index (GI) [26], Porta index (PI) [27], and the local distribution entropy [6].…”

Section: Introductionmentioning

confidence: 99%

Temporal Variation Measure Analysis: An Improved Second‐Order Difference Plot

Diao

Cai

2022

Complexity

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In this study, an improved second-order difference plot is proposed to analyze the variability of heart rate variability. Although the variation of the physiological status of the cardiovascular system can be shown graphically by the second-order difference plot, the descriptive ability of existing indicators for this plot is insufficient. As a result, the physiological information contained in the second-order difference plot cannot be extracted adequately. Addressing the problem, the temporal variation measure analysis is presented to describe distribution patterns of scatter points in the second-order difference plot quantitatively and extract the acceleration information for a variation of heart rate variability. Experiment results demonstrate the effectiveness of the temporal variation measure analysis. As a quantitative indicator, the temporal variation entropy is properly designed and successfully applied in the recognition and classification of the physiological statuses of the heart.

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Geometric patterns of time‐delay plots from different cardiac rhythms and arrhythmias using short‐term EKG signals

Cited by 8 publications

References 21 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 machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs

Temporal Variation Measure Analysis: An Improved Second‐Order Difference Plot

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