Distinguishing normal and abnormal heart rate variability using graphical and non-linear analyses

Stein, PK; Hui, Nelson; Domitrovich, Peter P.; Gottdiener, John S.; Rautaharju, P. M.

doi:10.1109/cic.2004.1442908

Cited by 2 publications

(3 citation statements)

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“…These patterns are seen even after careful scanning, with attention paid to uniform detection of beat onsets, labeling of ectopic beats, exclusion of intervals with blocked atrial premature complexes and exclusion of subjects with wandering atrial pacemaker, concealed block, and other rhythms too irregular to identify accurately the normal‐to‐normal intervals on which HRV measures are based. Such patterns can be identified on heart rate tachograms, plots of the heart rate power spectrum or Poincaré plots, which are plots of the interval between each pair of beats versus the interval between the next pair 6 . The cause of this erratic heart rhythm is not clear.…”

Section: Editorial Commentmentioning

confidence: 99%

“…Such patterns can be identified on heart rate tachograms, plots of the heart rate power spectrum or Poincaré plots, which are plots of the interval between each pair of beats versus the interval between the next pair. 6 The cause of this erratic heart rhythm is not clear. It has been hypothesized to be due to increased sympathetic activity, subtle atrial arrhythmias (too subtle to be seen on Holter scanning), subclinical sick sinus syndrome, or a loss of integration of complex physiologic feedback loops.…”

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Vagal Tone: Myths and Realities

Stein

2005

Cardiovasc electrophysiol

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The possibility, heard during a conference session while I was a graduate student, of measuring vagal tone from heart rate changes during respiration, hooked me into heart rate variability research. Vagal tone seemed to be a sort of Holy Grail. Anything that promised to increase it would be life sustaining, and that claim would be testable in an objective way. Loss of vagal tone could serve as an early warning signal and catastrophe could be prevented. The truth has proved to be far more complex, but it is my impression, as a reviewer of numerous manuscripts in the field that the essence of this myth persists. Where then does a more nuanced view of this field take us?The first issue is the confusion between vagal tone and what is really measurable from heart rate variability (HRV) that is vagal modulation of heart rate. Indeed, I am embarrassed to admit that one of the first review articles I wrote in this field (in 1993) was entitled "Heart rate variability: A measure of cardiac autonomic tone." 1 Malik and Camm clarified this distinction in the same year in their article "Components of heart rate variability-What they really mean and what we really measure." 2 Understanding the difference between vagal tone, as directly measured by vagus nerve activity and vagal modulation as measured by HRV is critical. Although direct measurement of peripheral (although, importantly not cardiac) sympathetic nerve traffic is now feasible, direct measurement of vagal activity is too invasive to be practical. Thus, we are limited to two measurements. Basal vagal tone can be estimated by giving atropine and measuring heart rate in the absence of all parasympathetic activity. This, however, does not provide any information about vagal tone under dynamic or stress conditions. Alternatively, vagal modulation of heart rate can be estimated, under controlled laboratory conditions or in an ambulatory setting, using appropriate manipulations of the beat-to-beat changes in heart rate and, as demonstrated in the current article by Kuo et al., 3 under certain circumstances, reflect vagus nerve traffic. However, the relationship between vagal activity and HRV does not appear to be linear, and a lack of vagally modulated HRV could indicate either a nonresponsive or a saturated system. In human studies, for example, Goldberger et al., 4 using phenylephrine and nitroprusside to stimulate and reduce vagally modulated baroreflex activity in subjects under beta-blockade, found that the relationship between HRV and parasympathetic effect was far from linear. They observed an ascending limb where HRV increased with increasing parasympathetic effect, followed by a plateau level. After that, HRV decreased with increasing parasympathetic effect. Moreover, this relationship was not uniform across subjects. A useful analogy might be to imagine that we can measure the height of the wave (modulation) but not the depth of the ocean (tone) using HRV.A second problem is equally important in clinical research. The various HRV indices that reflect vagal modula...

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Vagal Tone: Myths and Realities

Stein

2005

Cardiovasc electrophysiol

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“…Several linear and non-linear techniques based on the analysis of biomedical signals have been explored in earlier studies (Davos et al, 2002;Stein et al, 2005;Voss et al, 2010). Deep learning methods have been developed to predict ventricular fibrillation episodes in cardiomyopathy patients (Tseng and Tseng, 2020), and to predict heart failure in patients with different levels of left ventricular ejection fraction (Alkhodari1 et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Baroreflex activity through the analysis of the cardio-respiratory variability influence over blood pressure in cardiomyopathy patients

Rodriguez,

Schulz,

Voss

et al. 2023

Front. Physiol.

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A large portion of the elderly population are affected by cardiovascular diseases. Early prognosis of cardiomyopathies remains a challenge. The aim of this study was to classify cardiomyopathy patients by their etiology based on significant indexes extracted from the characterization of the baroreflex mechanism in function of the influence of the cardio-respiratory activity over the blood pressure. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM—24 patients) and dilated (DCM—17 patients) were considered. In addition, thirty-nine control (CON) subjects were used as reference. The beat-to-beat (BBI) time series, from the electrocardiographic (ECG) signal, the systolic (SBP), and diastolic (DBP) time series, from the blood pressure signal (BP), and the respiratory time (TT), from the respiratory flow (RF) signal, were extracted. The three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. DCM patients presented specific patterns in the respiratory response to decreasing blood pressure activity. ICM patients presented more stable cardiorespiratory activity in comparison with DCM patients. In general, CMP shown limited ability to regulate changes in blood pressure. In addition, patients also shown a limited ability of their cardiac and respiratory systems response to regulate incremental changes of the vascular variability and a lower heart rate variability. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. When comparing ICM patients and CON subjects, the best model achieved 88.9% accuracy, 87.5% sensitivity, and 89.7% specificity. When comparing DCM patients and CON subjects, the best model achieved 87.5% accuracy, 76.5% sensitivity, and 92.3% specificity. In conclusion, this study introduced a new method for the classification of patients by their etiology based on new indices from the analysis of the baroreflex mechanism.

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Distinguishing normal and abnormal heart rate variability using graphical and non-linear analyses

Cited by 2 publications

References 9 publications

Vagal Tone: Myths and Realities

Vagal Tone: Myths and Realities

Baroreflex activity through the analysis of the cardio-respiratory variability influence over blood pressure in cardiomyopathy patients

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