The next heartbeat: Creating dynamic and histographic Poincaré plots for the assessment of cardiac rhythms

Flanders, Wyatt H.; Moı̈se, N. Sydney; Pariaut, Romain; Sargent, Julia

doi:10.1016/j.jvc.2022.04.003

Cited by 3 publications

(12 citation statements)

References 34 publications

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“…SDNN in time domain analysis is known to be influenced by the duration of analysis. In short-term recordings, the primary source of SDNN variability is parasympathetically mediated RSA, whereas in 24-h recordings, sympathetic nerves contribute significantly to SDNN ( 20 , 31 ). Therefore, it is expected that the difference in analysis time between 24-h HRV and 1-h sleep HRV would impact SDNN values.…”

Section: Discussionmentioning

confidence: 99%

“…This plot can be analyzed by fitting an ellipse to the plotted points. The standard deviation of the distance of each point from the y = x axis was measured as SD1 (width of ellipse), While the standard deviation of the distance of each point from y = x + mean R-R interval was measured as SD2 (length of ellipse) (26)(27)(28)(29)(30)(31). The ratio SD1/SD2 was measured to assess autonomic balance.…”

Section: Nonlinear Heart Rate Variabilitymentioning

confidence: 99%

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Exploring sleep heart rate variability: linear, nonlinear, and circadian rhythm perspectives

Hasegawa,

Sasaki,

Umemoto

et al. 2024

Front. Vet. Sci.

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BackgroundHeart rate variability (HRV) is believed to possess the potential for disease detection. However, early identification of heart disease remains challenging, as HRV analysis in dogs primarily reflects the advanced stages of the disease.Hypothesis/objectiveThe aim of this study is to compare 24-h HRV with sleep HRV to assess the potential utility of sleep HRV analysis.AnimalsThirty healthy dogs with no echocardiographic abnormalities were included in the study, comprising 23 females and 7 males ranging in age from 2 months to 8 years (mean [standard deviation], 1.4 [1.6]).MethodsThis study employed a cross-sectional study. 24-h HRV and sleep HRV were measured from 48-h Holter recordings. Both linear analysis, a traditional method of heart rate variability analysis, and nonlinear analysis, a novel approach, were conducted. Additionally, circadian rhythm parameters were assessed.ResultsIn frequency analysis of linear analysis, the parasympathetic index nHF was significantly higher during sleep compared to the mean 24-h period (mean sleep HRV [standard deviation] vs. mean 24 h [standard deviation], 95% confidence interval, p value, r-family: 0.24 [0.057] vs. 0.23 [0.045], 0.006–0.031, p = 0.005, r = 0.49). Regarding time domain analysis, the parasympathetic indices SDNN and RMSSD were also significantly higher during sleep (SDNN: 179.7 [66.9] vs. 156.6 [53.2], 14.5–31.7, p < 0.001, r = 0.71 RMSSD: 187.0 [74.0] vs. 165.4 [62.2], 13.2–30.0, p < 0.001, r = 0.70). In a geometric method of nonlinear analysis, the parasympathetic indices SD1 and SD2 showed significantly higher values during sleep (SD1: 132.4 [52.4] vs. 117.1 [44.0], 9.3–21.1, p < 0.001, r = 0.70 SD2: 215.0 [80.5] vs. 185.9 [62.0], 17.6–40.6, p < 0.001, r = 0.69). Furthermore, the circadian rhythm items of the parasympathetic indices SDNN, RMSSD, SD1, and SD2 exhibited positive peaks during sleep.ConclusionThe findings suggest that focusing on HRV during sleep can provide a more accurate representation of parasympathetic activity, as it captures the peak circadian rhythm items.

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Section: Discussionmentioning

confidence: 99%

Section: Nonlinear Heart Rate Variabilitymentioning

confidence: 99%

Exploring sleep heart rate variability: linear, nonlinear, and circadian rhythm perspectives

Hasegawa,

Sasaki,

Umemoto

et al. 2024

Front. Vet. Sci.

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“…These results may be considered in risk assessment along with other biomarkers for dogs with MMVD especially since easy-to-use software for geometrical analysis of HRV are already available (Flanders et al. 2022 ). However, a widely accepted classification of the Poincaré plot shape is not yet available and future studies are needed for exploring the association between different biomarkers and beat-to-beat distribution during this pathological condition.…”

Section: Discussionmentioning

confidence: 99%

“…Briefly, the raw R–R data from 5 min ECG tracing were uploaded in a free geometrical HRV analysis software (Flanders et al. 2022 ) and an epoch of 50 successive R–R intervals was selected. Then, the epoch was moved from the onset to the end of the 5 min period analyzing in real time the shape of the Poincaré plot.…”

Section: Animals and Methodsmentioning

confidence: 99%

Analysis of Poincaré plot derived from 5-min electrocardiography in dogs with myxomatous mitral valve disease

Baisan

Turcu

Condurachi

et al. 2023

Veterinary Quarterly

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Background Evaluation of heart rate variability (HRV) is used for risk assessment in a variety of cardiac diseases including myxomatous mitral valve degeneration (MMVD). Objectives To compare the geometric analysis of HRV using visual patterns of Poincaré plot among different classes of MMVD in dogs and to analyse the differences in beat-to-beat variability using tachograms and sequential Poincaré plots among different shapes. Animals and methods Healthy and MMVD dogs were retrospectively reviewed. Five-minute ECG data was used to create Poincaré plots and shapes were compared among groups. Furthermore, a sub-analysis of 50 consecutive R–R intervals was performed. Pearson Chi-square with adjusted standardized residuals was used to compare the categorical data between groups. Results Fifteen healthy dogs and 157 dogs with MMVD were included in the study. Normal and B1 groups showed a predominance of triangular shape (73% and 60% respectively; p < 0.05). In B2 group the predominant shape was comet (40%; p < 0.05) while comet and torpedo were predominant in Ca group (41% and 36% respectively; p < 0.05). Visual geometric analysis revealed a lower dispersion of the cloud clustering towards the left lower corner of the plot with MMVD progression. Diamond and triangle revealed a lower mean heart rate compared to comet and torpedo shapes ( p < 0.01). Interclass correlation between 3 observers was 0.906 (95% CI of 0.8 − 0.96). Conclusions Poincaré plot shape changes with MMVD progression suggesting that geometrical analysis of HRV in dogs with cardiac conditions could be a useful tool in the risk assessment and further studies are warranted.

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“…Tachograms and Poincaré plots were created using software previously reported 25 . RR interval data was exported from the Trillium software and uploaded to the open‐access website: https://www.thenextheartbeat.com/.…”

Section: Methodsmentioning

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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The next heartbeat: Creating dynamic and histographic Poincaré plots for the assessment of cardiac rhythms

Cited by 3 publications

References 34 publications

Exploring sleep heart rate variability: linear, nonlinear, and circadian rhythm perspectives

Exploring sleep heart rate variability: linear, nonlinear, and circadian rhythm perspectives

Analysis of Poincaré plot derived from 5-min electrocardiography in dogs with myxomatous mitral valve disease

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

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