Heart Rate Fragmentation Research Articles

Increased heart rate fragmentation in those with Williams-Beuren syndrome suggests nonautonomic mechanistic contributors to sudden death risk.

Williams-Beuren syndrome (WBS) is a rare genetic condition caused by a chromosomal microdeletion at 7q11.23. It is a multisystem disorder characterized by distinct facies, intellectual disability, and supravalvar aortic stenosis (SVAS). Those with WBS are at increased risk of sudden death, but mechanisms underlying this remain poorly understood. We recently demonstrated autonomic abnormalities in those with WBS that are associated with increased susceptibility to arrhythmia and sudden cardiac death (SCD). A recently introduced method for heart rate variability (HRV) analysis called "heart rate fragmentation" (HRF) correlates with adverse cardiovascular events (CVEs) and death in studies where heart rate variability (HRV) failed to identify high-risk subjects. Some argue that HRF quantifies nonautonomic cardiovascular modulators. We, therefore, sought to apply HRF analysis to a WBS cohort to determine 1) if those with WBS show differences in HRF compared with healthy controls and 2) if HRF helps characterize HRV abnormalities in those with WBS. Similar to studies of those with coronary artery disease (CAD) and atherosclerosis, we found significantly higher HRF (4 out of 7 metrics) in those with WBS compared with healthy controls. Multivariable analyses showed a weak-to-moderate association between HRF and HRV, suggesting that HRF may reflect HRV characteristics not fully captured by traditional HRV metrics (autonomic markers). We also introduce a new metric inspired by HRF methodology, significant acute rate drop (SARD), which may detect vagal activity more directly. HRF and SARD may improve on traditional HRV measures to identify those at greatest risk for SCD both in those with WBS and in other populations.NEW & NOTEWORTHY This work is the first to apply heart rate fragmentation analyses to individuals with Williams syndrome and posits that the heart rate fragmentation parameter W3 may enable detection and investigation of phenomena underlying the proarrhythmic short-long-short RR interval sequences paradigm known to precede ventricular fibrillation and ventricular tachycardia. It also forwards a novel method for quantifying sinus arrhythmia and sinus pauses that likely correlate with parasympathetic activity.

Assessing heart rate fragmentation to predict atrial fibrillation in the general population aged 65

Abstract Rationale Screening the general population aged 65 for atrial fibrillation (AF) has been proposed as a preventive measure against potential complications. The dysfunction of the autonomic nervous system (ANS) plays an important role in atrial fibrillation (AF). Short-term HRV is conceptually attributed to physiologic vagal tone modulation. But recent data suggest that a component of short-term HRV, called heart rate fragmentation (HRF), is related to ANS breakdown and may be a predictor of AF occurrence. Metrics derived from heart rate variability (HRV) that depict heart rate fragmentation (HRF) have been suggested to reflect autonomic nervous system (ANS) dysfunction. Objectives To assess the predictive capacity of HRF markers for AF occurrence over an 18-year follow-up in individuals aged 65 at the study’s inspection and to develop a predictive score for AF onset. Methods A cohort study was designed to prospectively assess the predictive value of ANS activity level among general population regarding cardiovascular events and mortality. 1011 subjects aged 65 were enrolled in the study from September 2000 to December 2002. The study population had no history of AF and a low cardiovascular risk. A median follow-up of 17.8 years was managed. HRV data were acquired by 24-h Holter electrcardiogram (ECG) monitoring at baseline and assessed by frequency domain and time domain methods. Heart rate fragmentation using the percentage of inflection points (PIP) and alpha 1-index (using fractal evaluation of NN intervals) were then calculated. The study outcome was the development of AF during follow-up, defined as a standard 12-lead ECG recording or Holter recording with ≥ 30 s of AF. Results AF was diagnosed in 123 patients during the 18-year follow-up, representing a cumulative incidence of 13.4%. Using multivariate analysis, the occurrence of AF was independently predicted by the male gender (HR=1.74, 95%CI:1.16-2.63, p<0.01), a heart rate below 70bpm (HR=1.85, 95%CI:1.18-2.90, p<0.01), an increased percentage of NN intervals that differ by more than 50 ms from the previous interval (pnn50) (HR=1.69, 95%CI:1.06-2.70, p=0.03), an increased percentage of inflection points (HR=2.80, 95%CI:1.66-4.72,p<0.01), and a reduced alpha 1 index (HR=1.61, 95%CI:1.03-2.53, p=0.04). Given these results, a predictive score was built up based on the 4 independent predictors, ranging from 0 to 6 (Graphical Abstract). The high-risk group, defined as having a PROOF-AF score of 4 or higher, exhibited a 3.3-fold higher risk of developing AF (Figure). The PROOF-AF score demonstrated superior predictive performance compared to other available risk scores for incident AF such as CHARGE-AF score, C2HEST score, FHS score, and JMC score. Conclusion HRF parameters, included in the PROOF-AF score, may be used to identify individuals aged 65 without pre-existing health conditions who are at high-risk of developing AF and assist population screening.Graphical abstractFigure

Predicting long-term occurrence of atrial fibrillation in the general population using the assessment of heart rate fragmentation – The PROOF-AF study

Predicting long-term occurrence of atrial fibrillation in the general population using the assessment of heart rate fragmentation – The PROOF-AF study

Heart Rate Fragmentation, Ambulatory Blood Pressure, and Coronary Artery Calcification: A Population-Based Study

BackgroundLittle is known regarding whether ultra-rapid patterns of heart rate variability (eg, heart rate fragmentation [HRF]) are associated with coronary artery calcification (CAC) in a general population. ObjectivesThis study aimed to assess the association between HRF and CAC, and whether these associations are independent of systolic blood pressure (SBP) levels. MethodsFrom SESSA (the Shiga Epidemiological Study of Subclinical Atherosclerosis), we used data from 24-hour ambulatory blood pressure monitoring to identify awake and asleep SBP levels, and data from concurrent 24-hour Holter monitoring to quantify HRF using the awake and asleep percentage of inflection points (PIP). CAC on computed tomography scanning was quantified using an Agatston score. We used multivariable binomial logistic regression to assess the associations of PIP and ambulatory SBP with the presence of CAC, as defined by Agatston score >0. ResultsOf the 508 participants in this study (mean age: 66.5 ± 7.3 years), 325 (64%) had CAC and 183 (36%) did not. In fully adjusted models of prevalent CAC that also included office SBP, the ORs with 95% CIs for awake PIP, awake SBP, asleep PIP, and asleep SBP were 1.23 (95% CI: 0.99–1.54), 1.40 (95% CI: 1.11–1.77), 1.31 (95% CI: 1.05–1.62), and 1.28 (95% CI: 1.02–1.60), respectively. There was no evidence of interaction between PIP and ambulatory SBP in association with CAC. Results were similar when other HRF indices instead of PIP were used. ConclusionsHigher HRF and SBP levels during sleep are each associated with the presence of CAC in a general male population.

Heart rate fragmentation and brain MRI markers of small vessel disease in MESA.

Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored. In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later. In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P=0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P=0.03). HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury.

Abstract 17825: Heart Rate Fragmentation Analysis May Suggest Non-Autonomic Markers of Sudden Cardiac Death Risk in Adults With Williams-Beuren Syndrome

Background: Williams-Beuren syndrome (WBS) is a rare genetic disorder caused by a chromosomal microdeletion (7q11.23). It confers increased arrhythmia and sudden cardiac death (SCD) risk. Mechanisms underlying this risk remain obscure. We recently found HRV abnormalities in individuals with WBS that are known to correlate with sudden death risk in diabetes and following MI and stroke. Heart rate fragmentation (HRF) correlated with adverse CV events in some studies where HRV failed to identify high-risk subjects. Some argue that HRF quantifies non-autonomic CV modulators. We, therefore, sought to further explore non-autonomic modulators of cardiovascular disease by performing HRF analysis in a cohort of adults with WBS. Hypothesis: We hypothesize that those with WBS may exhibit increased sudden death risk from mechanisms not detected by conventional HRV measures. Aims: We aim to 1) Identify biomarkers that are predictive of SCD risk in WBS, and 2) Determine if HRF quantifies non-autonomic CV modulators. Methods: We performed HRF analyses on 24hr ECGs in subjects with WBS (n=18) and age- and sex-matched controls (n=18) enrolled in a prospective NIH study. HRF analyses include the percentage of inflection points (PIP), percentages of hard (PIP H ) and soft (PIP S ) inflection points, and the distribution of “words”, where W 0 ,W 1 ,W 2 , W 3 represent sequences of 4 beats with 0, 1, 2, or 3 inflection points. Inflection points were defined as a change in NN by more than 1/s, where ‘s’ is the sampling frequency. Mann-Whitney tests were used to compare HRF in subjects vs. controls. Spearman’s correlation was used to compare HRF to traditional HRV. Results: HRF markers (PIP, PIP S , and W 3 ) were significantly elevated in WBS subjects (P<0.003)(Fig 1A-C). PIP S and W 3 were negatively correlated with parasympathetic markers HF power and SD1 among WBS subjects (Fig 1D). Conclusion: HRF may quantify some non-autonomic modulators of sudden death risk in those with WBS.

O040 Assessment of Heart Rate Variability During Respiratory Events and Arousals Using Symbolic Dynamics

Abstract Introduction Beat-to-beat heart rate dynamics vary during obstructive respiratory events and in both respiratory and spontaneous arousals. However, conventional quantifications of heart rate variability (HRV) require segments of data substantially longer than the typical respiratory event or arousal. Subsequently, previous studies investigating dynamics in these significantly shorter segments have utilised simple quantifications such as peak heart rate. This study aimed to more-richly characterise HRV during obstructive respiratory events and arousals using symbolic dynamics. Methods One hundred individuals with suspected obstructive sleep apnoea (55 male, 57.1±12.9 years) were studied with diagnostic polysomnography, including electrocardiography. R-wave intervals were calculated and extracted during respiratory events terminating with/without arousals and during spontaneous and respiratory arousals. The symbolic dynamics tool “heart rate fragmentation” was applied to quantify HRV during the events, whereby the percentage of inflection points (PIP), defined as beat-to-beat transitions from heart-rate acceleration to heart-rate deceleration, were calculated. Statistical analysis was conducted with the Wilcoxon rank sum test. Results PIP was higher during respiratory events terminated with arousal compared with those without arousal (52.9±6.4% vs. 48.5±6.7%, P<0.05). PIP was also higher during spontaneous arousals than during respiratory arousals (51.3±6.1% vs. 45.8±6.7%, P<0.05). Discussion Previous mechanistic experiments have associated increased PIP with greater parasympathetic activity. Thus, our preliminary results suggest greater parasympathetic activity during respiratory events terminated by arousal than those without; and during spontaneous arousals compared with respiratory arousals. Further work is required to understand the pathophysiology underlying this and other similar metrics, potentially leading to novel obstructive sleep apnoea-related cardiorespiratory pathology biomarkers.

ECG-Based Driving Fatigue Detection Using Heart Rate Variability Analysis with Mutual Information

One of the WHO’s strategies to reduce road traffic injuries and fatalities is to enhance vehicle safety. Driving fatigue detection can be used to increase vehicle safety. Our previous study developed an ECG-based driving fatigue detection framework with AdaBoost, producing a high cross-validated accuracy of 98.82% and a testing accuracy of 81.82%; however, the study did not consider the driver’s cognitive state related to fatigue and redundant features in the classification model. In this paper, we propose developments in the feature extraction and feature selection phases in the driving fatigue detection framework. For feature extraction, we employ heart rate fragmentation to extract non-linear features to analyze the driver’s cognitive status. These features are combined with features obtained from heart rate variability analysis in the time, frequency, and non-linear domains. In feature selection, we employ mutual information to filter redundant features. To find the number of selected features with the best model performance, we carried out 28 combination experiments consisting of 7 possible selected features out of 58 features and 4 ensemble learnings. The results of the experiments show that the random forest algorithm with 44 selected features produced the best model performance testing accuracy of 95.45%, with cross-validated accuracy of 98.65%.

Heart rate fragmentation as a marker of altered activity of the autonomic nervous system activity : a novel predictor of atrial fibrillation occurrence in the general population

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Unrestricted grant for fellowship support - Abbott Medical Background The dysfunction of the autonomic nervous system (ANS) plays an important role in atrial fibrillation (AF). However, the meaning of high heart rate variability (HRV) in predicting AF is still under debate due to conflicting results in population-based studies. Short-term HRV is conceptually attributed to physiologic vagal tone modulation. But recent data suggest that a component of short-term HRV, called heart rate fragmentation, is related to ANS breakdown and not to the parasympathetic activity. Purpose – To assess the predictive capacity of novel short-term HRV-derived metrics regarding the occurrence of AF in the general population during long-term follow-up. Methods – A cohort study was designed to prospectively assess the predictive value of ANS activity level among general population regarding cardiovascular events and mortality. 1011 subjects aged 65 were enrolled in the study from September 2000 to December 2002. The study population had no history of AF and a low cardiovascular risk. A median follow-up of 17.8 years was managed. HRV data were acquired by 24-h Holter electrocardiogram (ECG) monitoring at baseline and assessed by frequency domain and time domain methods. Heart rate fragmentation using the percentage of inflection points (PIP) and alpha 1-index (using fractal evaluation of NN intervals) were then calculated. The study outcome was the development of AF during follow-up, defined as a standard 12-lead ECG recording or Holter recording with ≥ 30 s of AF. Results – AF was diagnosed in 123 patients during the 18-year follow-up, representing a cumulative incidence of 13.4%. Using multivariate analysis, the occurrence of AF was independently predicted by the male gender (HR=1.96, 95%CI:1.31-2.94, p<0.01), a higher percentage of NN intervals that differ by more than 50 ms from the previous interval (pnn50) (HR=2.01, 95%CI:1.27-3.18, p<0.01), an increased percentage of inflection points (HR=2.48, 95%CI:1.47-4.18,p<0.01), and a reduced alpha 1 index (HR=1.63, 95%CI:1.04-2.55, p<0.01). Given these results, a predictive score was built up based on the 4 independent predictors (Figure 1A). A predictive score of AF occurrence during the 18-year follow-up was assigned to every patient from 0 to 4. Compared to the group of patients with a score of 0 (Figure 1B), AF occurrence was progressively increased in patients with a score of 2 (n=390, HR=5.93, 95%CI:0.82-43.08, p=0.08), of 3 (n=175, HR=12.75., 95%CI:1.75-92.83, p=0.01), and of 4 (n=55, HR=22.31, 95%CI:2.98-167.23, p<0.01). Conclusion (Figure 2) – Different markers of short-term HRV predict independently the AF occurrence in the general population aged 65, including markers of ANS breakdown gathered in metrics of heart rate fragmentation, such as an increased PIP and a reduced alpha 1-index. A clinical predictive score based on these predictors is able de segregate a population at high risk of AF occurrence among the general population.

Heart rate fragmentation in renovascular hypertensive rats

Although rapid oscillations of the heart rate (HR) are attributed to vagal modulation of the heart, a pattern of ultra-rapid HR variation has been named heart rate fragmentation (HRF). It is a recently proposed approach to evaluate sino-atrial instability characterized by the presence of numerous inflections points in a series of successive values of cardiac intervals. HRF is increased in aging and coronary diseases. Hypertension markedly alters HRV indices and might affect HRF as well. This study aimed to investigate the HRF in renovascular hypertension in rats. Wistar rats were anesthetized, and hypertension was surgically induced by partial constriction of the left renal artery with a silver clip with a 0.2 mm gap. In one kidney one clip (1K1C) hypertension model, the contralateral kidney was removed. In two kidneys one clip (2K1C) model, the contralateral kidney was kept intact. Sham-operated control rats underwent to same surgical procedures without receiving the clip around the renal artery. After 45 days (2K1C) or 60 days (1K1C), animals were instrumented with a catheter into the femoral artery, and on the following day, their arterial pressure (AP) was directly recorded without the effect of anesthesia. For HRF analysis, pulse interval (PI) series were generated and transformed into a sequence of symbols “-1,” “0,” or “1” when the difference between successive PI values (transitions) was negative, zero, or positive, respectively. Following, sequences of 4 consecutive symbols were classified according to the number of transitions. The total percentage of inflection points (PIP) and the percentage of sequences with zero (W0), one (W1), two (W2), or three (W3) inflection points were quantified. As expected, the arterial pressure increased in both groups of hypertension rats compared to control counterparts. All HRF indices were found similar in 1K1C hypertensive rats compared to their control counterparts. Nevertheless, in 2K1C hypertensive rats HRF was found to be lower than normotensive controls (PIP: 64±7 vs. 79±1% and W2: 30±1 vs. 39±1% in 2K1C and normotensive controls, respectively). In conclusion, high blood pressure itself does not affect HRF, although, depending on the model of hypertension used, HRF can be markedly impaired. The interpretation of these findings is challenging, and the high levels of angiotensin in the 2K1C model may affect cardiac pacemaker cells, making their action potentials more stable than unstable. However, the specific mechanisms by which this could happen still need to be studied. Funding Sources: FAPESP, CAPES and CNPq This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

PO-01-202 HEART RATE FRAGMENTATION AS A MARKER OF ALTERED ACTIVITY OF THE AUTONOMIC NERVOUS SYSTEM ACTIVITY : A NOVEL PREDICTOR OF ATRIAL FIBRILLATION OCCURRENCE IN THE GENERAL POPULATION

The dysfunction of the autonomic nervous system (ANS) plays a crucial role in atrial fibrillation (AF). But the meaning of high heart rate variability (HRV) in predicting AF is still under debate. Short-term HRV is conceptually attributed to physiologic vagal tone modulation. But recent data suggest that a component of short-term HRV, called heart rate fragmentation, is related to ANS breakdown. To assess the predictive capacity of novel short-term HRV-derived metrics regarding the occurrence of AF in the general population during long-term follow-up. A cohort study was designed to prospectively assess the predictive value of ANS activity level regarding cardiovascular events and mortality. 1011 subjects aged 65, without history of AF, and with low cardiovascular risk were enrolled in the study. A 17.8-year follow-up was managed. HRV data were acquired by 24-h Holter ECG monitoring at baseline and assessed by frequency and time-domain methods. Heart rate fragmentation using the percentage of inflection points (PIP) and alpha 1-index (using fractal evaluation of NN intervals) were also calculated. AF occurrence during follow-up was registered and defined as a standard 12-lead ECG recording or Holter recording with ≥ 30 s of AF. AF was diagnosed in 123 patients representing a cumulative incidence of 13.4%. Univariate analyses are presented in the Table. The occurrence of AF was independently predicted by the male gender (HR=1.96,95%CI:1.3-2.9,p<0.01), a higher percentage of NN intervals that differ by more than 50 ms from the previous interval (pnn50) (HR=2.01,95%CI:1.3-3.2,p<0.01), an increased percentage of inflection points (HR=2.48,95%CI:1.5-4.2,p<0.01), and a reduced alpha 1 index (HR=1.63,95%CI:1.04-2.55,p<0.01). Given these results, a predictive score was built up based on the 4 independent predictors (Panel A). A predictive score of AF occurrence during the 18-year follow-up was assigned to every patient from 0 to 4. Compared to the group of patients with a score of 0 (Panel B), AF occurrence was progressively increased in patients with a score of 2 (HR=5.93, 95%CI:0.82-43.08, p=0.08), of 3 (HR=12.75, 95%CI:1.75-92.83, p=0.01), and of 4 (HR=22.31, 95%CI:2.98-167.23, p<0.01). Markers of heart rate fragmentation independently predict the AF occurrence in the general population, such as an increased PIP and a reduced alpha 1-index. A clinical predictive score is able de segregate a population at high risk of AF occurrence among the general population.Tabled 1Univariate analysesVariablesHRCI 95%P-valueCLINICAL FEATURESMale gender1.741.22 - 2.48<0.01Hipertension1.741.22 - 2.50<0.01Overweight1.331.04 - 1.700.02Increased PAC burden2.111.31 - 3.41<0.01ANS FEATURESpnn501.841.28 - 2.64<0.01RMSSD1.901.33 - 2.71<0.01LF/HF0.600.39 - 0.940.02PIP2.201.40 - 3.47<0.01Alpha 12.191.54 - 3.12<0.01Deceleration capacity2.001.30 - 3.09<0.01 Open table in a new tab

The connection between heart rate variability (HRV), neurological health, and cognition: A literature review.

The heart and brain have bi-directional influences on each other, including autonomic regulation and hemodynamic connections. Heart rate variability (HRV) measures variation in beat-to-beat intervals. New findings about disorganized sinus rhythm (erratic rhythm, quantified as heart rate fragmentation, HRF) are discussed and suggest overestimation of autonomic activities in HRV changes, especially during aging or cardiovascular events. When excluding HRF, HRV is regulated via the central autonomic network (CAN). HRV acts as a proxy of autonomic activity and is associated with executive functions, decision-making, and emotional regulation in our health and wellbeing. Abnormal changes of HRV (e.g., decreased vagal functioning) are observed in various neurological conditions including mild cognitive impairments, dementia, mild traumatic brain injury, migraine, COVID-19, stroke, epilepsy, and psychological conditions (e.g., anxiety, stress, and schizophrenia). Efforts are needed to improve the dynamic and intriguing heart-brain interactions.

Heart rate fragmentation as a marker of altered autonomic nervous system activity: A novel predictor of atrial fibrillation occurrence in the general population

Heart rate fragmentation as a marker of altered autonomic nervous system activity: A novel predictor of atrial fibrillation occurrence in the general population

Heart rate fragmentation is impaired in type 2 diabetes mellitus patients

AimsTo evaluate the heart rate fragmentation (HRF) of type 2 diabetes mellitus (T2DM) patients and its relationship with heart rate variability (HRV) indices. MethodsOne hundred sixty-four men, aged 47–57 years were retrospectively analyzed from a database. Participants were T2DM (n = 82) and apparently healthy (n = 82). R-R interval time series recorded by electrocardiogram were collected at the supine position for 10 to 15 min. From HRF, the percentage of inflection points (PIP), percentage of words with zero, one, two, or three inflections points (W0, W1, W2, W3), and percentage with only type hard, soft, or mixed inflections points type (WH, WS, WM) were quantified. ResultsT2DM presented higher PIP, WS, WM and W3, while WH and W1 was lower compared with healthy (p < 0.05). Moreover, a positive moderate correlation was found between WH and root mean square of the successive R-R differences (RMSSD) and high frequency (HF) indices. In contrast, a negative moderate correlation was found between WS and WM with RMSSD and HF indices. ConclusionsT2DM have increased fragmentation patterns, and words grouped by inflection type are more closely related to HRV. The HRF approach might be useful to assess heart rate dynamic abnormalities in males with type 2 diabetes.

Method to quantify the impact of sleep on cardiac neuroautonomic functionality: application to the prediction of cardiovascular events in the Multi-Ethnic Study of Atherosclerosis.

We introduce the concept of cardiac neuroautonomic renewability and a method for its quantification. This concept refers to the involuntary nervous system's capacity to improve cardiac control in response to restorative interventions, such as sleep. We used the change in heart rate fragmentation (ΔHRF), before sleep onset compared with after sleep termination, to quantify the restorative effects of sleep. We hypothesized that the ability to improve cardiac neuroautonomic functionality would diminish with age and be associated with lower risk of major adverse cardiovascular events (MACE). We analyzed the ECG channel of polysomnographic recordings from an ancillary investigation of the Multi-Ethnic Study of Atherosclerosis (MESA). In a cohort of 659 participants (mean ± SD age, 69.7 ± 8.8; 42% male), HRF was significantly (P < 0.001) lower after sleep (before: 74 ± 12%, after: 67 ± 13%). Furthermore, the magnitude of the decrease significantly (P < 0.001) diminished with cross-sectional age. In addition, a larger reduction in HRF following sleep (i.e., higher ΔHRF) was associated with lower risk of MACE, independent of traditional cardiovascular risk factors and current measures of sleep quality. Specifically, over a mean follow-up period of 6.4 ± 1.6 yr, in which 60 participants had their first MACE, a one-SD (12%) increase in ΔHRF was associated with a 36% (95% CI: 12%-53%) decrease in the risk of MACE. The results demonstrate the restorative impact of sleep on heart rate control. As such they support the concept of cardiac neuroautonomic renewability and the utility of ΔHRF for its quantification.

Heart rate fragmentation as a marker of altered global autonomic nervous system activity: a novel predictor of atrial fibrillation occurrence in the general population

Abstract Background The dysfunction of the autonomic nervous system (ANS) plays an important role in the initiation and maintenance of atrial fibrillation (AF). However, the meaning of high heart rate variability (HRV) in predicting AF is still under debate due to conflicting results in population-based studies. Short-term HRV is conceptually attributed to vagal tone modulation. But recent data suggest that a component of short-term HRV, called heart rate fragmentation, is related to ANS breakdown. On the other hand, parasympathetic activity could be assess by a novel metric, the deceleration capacity (DC). Objective To assess predictive capacity of novel HRV-derived metrics, whether fragmentation indices or parameters of vagal tone, regarding the occurrence of AF in general population during long-term follow-up. Methods A cohort study was designed to prospectively assess the predictive value of ANS activity level among a healthy retired French population, regarding cardiovascular events and mortality. 1011 subjects aged 65 were enrolled in the study from 2000 to 2002. The enrolled population had no history of AF and a low cardiovascular risk. A median follow-up of 17.8 years (16.0–18.5) was managed. HRV data were acquired by 24-h Holter electrocardiogram (ECG) monitoring at baseline and assessed by frequency domain and time domain methods. Heart rate fragmentation using the percentage of inflection points (PIP), and assessment of specific parasympathetic activity using DC were then calculated. The study outcome was the onset of AF during follow-up, defined as a standard 12-lead ECG or Holter recording with ≥30 s of AF. Results AF was diagnosed in 123 patients along the 18-year follow-up, representing a cumulative incidence of 13.4%. Using univariate analysis (Figure 1A), a higher percentage of NN intervals that differ by more than 50 ms from the previous interval (pnn50) and root mean square differences of successive NN intervals (RMSSD) were associated with AF onset (p&amp;lt;0.01). A lower ratio of low-frequency and high-frequency component (LF/HF) was the only frequency-domain parameter found to predict the AF occurrence (p&amp;lt;0.01). The occurrence of AF was predicted by a higher PIP (p&amp;lt;0.01) and lower DC (p&amp;lt;0.01). Through multivariate analysis using clinical parameters that reached statistical significance in univariate analysis (Figure 1B), higher PIP was found as an independent predictor of AF occurrence (HR=2.1, 95% CI: 1.1–3.9, p=0.02), as well as male gender (HR=2.1, 95% CI: 1.1–3.9, p=0.02), and non-sustained supraventricular tachycardia (HR=2.1, 95% CI: 1.1–3.9, p=0.02). Conclusion (see Figure 2) Elevated PIP, a marker of heart rate fragmentation secondary to impaired ANS activity, is an independent predictor of the onset of AF in the healthy general population. Therefore, high HRV associated to AF occurrence could be the consequence of general ANS impairment rather than increased parasympathetic activity. Funding Acknowledgement Type of funding sources: None.

Correlation between heart rate variability and polysomnography-derived scores of obstructive sleep apnea.

Obstructive sleep apnea (OSA) is one of the most common sleep disorders and affects nearly a billion people worldwide. Furthermore, it is estimated that many patients with OSA are underdiagnosed, which contributes to the development of comorbidities, such as cardiac autonomic imbalance, leading to high cardiac risk. Heart rate variability (HRV) is a non-invasive, widely used approach to evaluating neural control of the heart. This study evaluates the relationship between HRV indices and the presence and severity of OSA. We hypothesize that HRV, especially the nonlinear methods, can serve as an easy-to-collect marker for OSA early risk stratification. Polysomnography (PSG) exams of 157 patients were classified into four groups: OSA-free (N = 26), OSA-mild (N = 39), OSA-moderate (N = 37), and OSA-severe (N = 55). The electrocardiogram was extracted from the PSG recordings, and a 15-min beat-by-beat series of RR intervals were generated every hour during the first 6h of sleep. Linear and nonlinear HRV approaches were employed to calculate 32 indices of HRV. Specifically, time- and frequency-domain, symbolic analysis, entropy measures, heart rate fragmentation, acceleration and deceleration capacities, asymmetry measures, and fractal analysis. Results with indices of sympathovagal balance provided support to reinforce previous knowledge that patients with OSA have sympathetic overactivity. Nonlinear indices showed that HRV dynamics of patients with OSA display a loss of physiologic complexity that could contribute to their higher risk of development of cardiovascular disease. Moreover, many HRV indices were found to be linked with clinical scores of PSG. Therefore, a complete set of HRV indices, especially the ones obtained by the nonlinear approaches, can bring valuable information about the presence and severity of OSA, suggesting that HRV can be helpful for in a quick diagnosis of OSA, and supporting early interventions that could potentially reduce the development of comorbidities.

Heart rate variability as a biomarker in patients with Chronic Chagas Cardiomyopathy with or without concomitant digestive involvement and its relationship with the Rassi score

BackgroundDysautonomia plays an ancillary role in the pathogenesis of Chronic Chagas Cardiomyopathy (CCC), but is the key factor causing digestive organic involvement. We investigated the ability of heart rate variability (HRV) for death risk stratification in CCC and compared alterations of HRV in patients with isolated CCC and in those with the mixed form (CCC + digestive involvement). Thirty-one patients with CCC were classified into three risk groups (low, intermediate and high) according to their Rassi score. A single-lead ECG was recorded for a period of 10–20 min, RR series were generated and 31 HRV indices were calculated. The HRV was compared among the three risk groups and regarding the associated digestive involvement. Four machine learning models were created to predict the risk class of patients.ResultsPhase entropy is decreased and the percentage of inflection points is increased in patients from the high-, compared to the low-risk group. Fourteen patients had the mixed form, showing decreased triangular interpolation of the RR histogram and absolute power at the low-frequency band. The best predictive risk model was obtained by the support vector machine algorithm (overall F1-score of 0.61).ConclusionsThe mixed form of Chagas' disease showed a decrease in the slow HRV components. The worst prognosis in CCC is associated with increased heart rate fragmentation. The combination of HRV indices enhanced the accuracy of risk stratification. In patients with the mixed form of Chagas disease, a higher degree of sympathetic autonomic denervation may be associated with parasympathetic impairment.

Heart Rate Fragmentation in Rats with Streptozotocin‐induced Diabetes

Heart rate fragmentation (HRF) is a recently proposed approach to evaluate sino‐atrial instability characterized by the presence of numerous inflections points in a series of successive values of RR intervals (RRi). HRF has been shown to be increased in aging and coronary diseases and seems to be linked to the risk of life‐threatening cardiovascular events and death. We recently demonstrated that cardiac autonomic modulation in involved in HRF in rats. Moreover, HRF is increased in a rat model of heart failure. Diabetes mellitus (DM) markedly alters HRV indices and might affect HRF as well. Therefore, we hypothesize that HRF is altered in streptozotocin‐ (STZ) induced diabetic rats. Male Sprague‐Dawley rats were injected with STZ (50 mg/kg), or vehicle (citrate buffer) into penile vein. Development of diabetes was confirmed 72h after STZ by the presence of hyperglycemia (&gt; 350 mg/dL). Electrocardiographic (ECG) recordings were performed in conscious rats, 7 days (acute) or 4 weeks (chronic) after STZ or vehicle administration. Series of RR intervals were generated and processed by the customized software PyBios as follows: RRi values were symbolized as “‐1”, “0,” or “1” when the differences between successive RRi were negative, null, or positive, respectively. Transitions between symbols “‐1” and “1” were labeled as “hard” (H) inflection points, while those between “‐1” (or “1”) and “0” were labeled as “soft” (S) inflection points. The percentage of inflection points (PIP) was quantified, as well as sequences of 4 consecutive symbols, so‐called “words”, computing the occurrence of words with zero (W0), one (W1), two (W2), or three (W3) inflections points. The occurrence of words with only hard (WH) or only soft transitions (WS) were also quantified. Both groups of diabetic rats were bradycardic as compared to their control counterparts. Data of HRF are shown in the table below. Occurrences (%, mean ± SEM) of PIP, words with 0 to 3 inflection points, and words with only soft or hard transitions of symbols. As hypothesized, diabetes affects HRF, increasing fragmented indices (PIP, W3 and WH) and decreasing fluent ones (W1 and WS). Moreover, our results showed that changes in HRF due to diabetes in this model is time‐dependent. In addition to traditional methods of analyzing heart rate variability, HRF is a new method that can contribute to the diagnosis and prognosis of cardiovascular diseases in diabetic patients. Further studies are necessary to understand better the mechanisms involved in our findings.

Heart Rate Fragmentation in Patients with Type 2 Diabetes Mellitus

Heart rate fragmentation (HRF) is a recently proposed approach to evaluate ultra‐rapid variability of heart rate. HRF has been shown to be increased in the aging process and in conditions such as coronary artery disease. Moreover, it proved to be a strong biomarker of adverse cardiovascular events, mortality and was an independent predictor of atrial fibrillation. Type 2 diabetes mellitus (DM) is a disease known to affect heart rate variability (HRV) indices markedly; however, HRF in diabetic patients is to be studied. We aimed to evaluate HRF in patients with DM, without autonomic cardiovascular neuropathy, compared to their healthy counterparts. Electrocardiographic (ECG) recordings collected from patients with diabetes (n=102) and healthy subjects (n=96), aged 44 to 59 years, were retrospectively analyzed from the Cardiovascular Physical Therapy Laboratory database. ECG were recorded at rest (supine) for 10 to 15 minutes. RR interval (RRi) series were processed by the customized software PyBios. RRi values were symbolized as “‐1”, “0,” or “1” when the differences between successive RRi were negative, null, or positive, respectively. The percentage of inflection points (PI) was quantified and sequences of 4 consecutive symbols, so‐called “words”, were analyzed computing the occurrence of words with zero (W0), one (W1), two (W2), or three (W3) inflections points. The occurrence of words with only hard (‐1 to 1: WH), soft (‐1 to 0 or 0 to 1: WS), or mixed (WM) (i.e., both H and S) transitions were also quantified. Patients with diabetes presented higher PIP (58±1 vs 56±1, p=0.033), W3(17±1 vs14±1, p&lt;0.001), WS(25±2 vs16±1, p&lt;0.001) and WM(22±1 vs 17±1, p&lt;0.001). On the other hand, patients with diabetes showed a lower percentage of W1 (31±1 vs 35±1, p=0.024) and WH (47±2 vs 61±2, p&lt;0.001). The occurrence of W0 and W2 were similar between groups. In conclusion, diabetics patients without autonomic cardiovascular neuropathy have augmented fragmentation of heart rate as compared to healthy subjects. Clinical applicability: In the future, studies with this approach might be useful to assess heart rate dynamic abnormalities in order to detect cardiac risks in patients with diabetes mellitus.