Heart rate variability

Malík, Marek; Camm, A. John

doi:10.1002/clc.4960130811

Cited by 341 publications

(138 citation statements)

References 60 publications

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“…For the purposes of the study, the testing was done over 5 minutes in very controlled conditions. A reduction of the SDNN is correlated with left ventricular dysfunction, and has been found to be predictive of increased risk of sudden cardiac death [21][22][23][24][25] and primarily reflects the intrinsic ability to respond to hormonal changes.…”

Section: Hrv Assessmentmentioning

confidence: 99%

Autonomic Dysfunction in Normal Tension Glaucoma: The Short-term Heart Rate Variability Analysis

Na¹,

Lee²,

Park

et al. 2010

Journal of Glaucoma

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Section: Hrv Assessmentmentioning

confidence: 99%

Autonomic Dysfunction in Normal Tension Glaucoma: The Short-term Heart Rate Variability Analysis

Na¹,

Lee²,

Park

et al. 2010

Journal of Glaucoma

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“…Recent literature has shown the effectiveness and the utility of heart period (HP) variability measures to study cardiac autonomic function [Malik and Camm, 1990;Huikuri et al, 1990;Malliani et al, 1991;Yeragani, 1995]. These noninvasive measures have been used in several different disciplines to study cardiac function in health as well as in disease.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have shown a definite link between decreased HP variability and significant cardiovascular mortality in patients with cardiac illness and also in normal controls [Kleiger et al, 1987;Huikuri et al, 1990;Malik and Camm, 1990;Molgaard et al, 1991;Malliani et al, 1995]. We have been studying heart rate (HR) and HP variability measures in patients with panic disorder since some studies suggest an increased mortality from cardiovascular illness in this group of patients [Coryell et al, 1986;Weissman et al, 1990;Kawachi et al, 1994aKawachi et al, ,b, 1995.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear measures of heart period variability: Decreased measures of symbolic dynamics in patients with panic disorder

et al. 2000

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Time series of heart period are not linear and recent studies illustrated the importance of using nonlinear methods to quantify the complexity of these time series. We compared different techniques to quantify the nonlinear complexity of these time series in patients with panic disorder and normal controls and correlated these measures with spectral powers in different bands of interest. Twenty‐four hour ECG was recorded in 23 normal controls and 29 patients with panic disorder by using Holter records. Time series of heart period were analyzed by using approximate entropies, slopes of 1/f scaling, two algorithms to calculate fractal dimension, and word sequences using symbolic dynamics. Measures using symbolic dynamics, especially word count (WC‐100), showed highly significant differences between the two groups similar to some of the frequency domain (spectral) measures, while the other techniques were relatively ineffective to distinguish between the two groups. Different nonlinear techniques may relate to different aspects of nonlinear complexity of the time series. These nonlinear techniques were also not uniform in showing the differences between awake and sleep periods. Some correlate with the measures of respiratory sinus arrhythmia and some measures obtained from symbolic dynamics may reflect not only the nonlinear complexity of the time series but also the total variability in the 24 hr HP time series, especially power in the ultra‐low frequency band (<0.0033 Hz). However, word count (WC‐100) had only weak correlations with other measures and discriminated best between the two groups and showed that this nonlinear measure was of additional value to the linear measures in classifying the two groups. Depression and Anxiety 12:67–77, 2000. © 2000 Wiley‐Liss, Inc.

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“…A low LF HRV may predict the progression of atherosclerosis in these patients. thetic innervation, whereas high frequency (HF) (0.15-0.40 Hz) oscillations indicate parasympathetic activity [1,29,34,37,38]. Reduced HRV is a feature of autonomic neuropathy.…”

mentioning

confidence: 99%

Decreased heart rate variability may predict the progression of carotid atherosclerosis in type 2 diabetes

et al. 2006

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Heart rate variability (HRV), a measure of autonomic function, can predict survival outcomes. Cardiovascular disease is a known complication of diabetes, and we aimed to determine if autonomic dysfunction was associated with carotid artery atherosclerotic plaques in type 2 diabetic patients. We assessed frequency domain HRV from power spectral analysis of 24 h Holter ECG recordings, expiration/inspiration (E/I) ratio during deep breathing, acceleration index (AI) of R-R interval in response to head-up tilt, and the degree of carotid artery atherosclerosis in 61 type-2 diabetic patients (39 males, 45-69 years). Studies were carried out 5-6 years after diagnosis (baseline) and repeated 8 years after diagnosis (follow-up). At baseline, patients diagnosed with autonomic neuropathy, with abnormal E/I ratio and abnormal AI measurements, had decreased low frequency (LF) HRV. Baseline E/I ratio correlated with day (r = 0.34; P < 0.001) and night-time (r = 0.44; P < 0.001) LF power. Night-time HRV did not differ in patient with and without autonomic neuropathy. Reduced common carotid artery diameter and atherosclerotic intima-media thickness (IMT) both correlated with HRV at baseline. At follow-up, all HRV variables decreased significantly. Furthermore, patients with lower LF power at baseline, had a larger increase in the thickness of the carotid bulb intima-media at follow-up. Our results show that LF HRV power is associated with the extent and progression of carotid atherosclerosis in type 2 diabetes. A low LF HRV may predict the progression of atherosclerosis in these patients.

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Heart rate variability

Cited by 341 publications

References 60 publications

Autonomic Dysfunction in Normal Tension Glaucoma: The Short-term Heart Rate Variability Analysis

Autonomic Dysfunction in Normal Tension Glaucoma: The Short-term Heart Rate Variability Analysis

Nonlinear measures of heart period variability: Decreased measures of symbolic dynamics in patients with panic disorder

Decreased heart rate variability may predict the progression of carotid atherosclerosis in type 2 diabetes

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