Heart Rate Fragmentation: A Symbolic Dynamical Approach

Heart rate variability and autonomic nervous system imbalance: Potential biomarkers and detectable hallmarks of aging and inflammaging

The most cutting-edge issue in the research on aging is the quest for biomarkers that transcend molecular and cellular domains to encompass organismal-level implications. We recently hypothesized the role of Autonomic Nervous System (ANS) imbalance in this context. Studies on ANS functions during aging highlighted an imbalance towards heightened sympathetic nervous system (SNS) activity, instigating a proinflammatory milieu, and attenuated parasympathetic nervous system (PNS) function, which exerts anti-inflammatory effects via the cholinergic anti-inflammatory pathway (CAP) and suppression of the hypothalamic-pituitary-adrenal (HPA) axis. This scenario strongly suggests that ANS imbalance can fuel inflammaging, now recognized as one of the most relevant risk factors for age-related disease development. Recent recommendations have increasingly highlighted the need for actionable strategies to improve the quality of life for older adults by identifying biomarkers that can be easily measured, even in asymptomatic individuals. We advocate for considering ANS imbalance as a biomarker of aging and inflammaging. Measures of ANS imbalance, such as heart rate variability (HRV), are relatively affordable, non-invasive, and cost-effective, making this hallmark easily diagnosable. HRV gains renewed significance within the aging research landscape, offering a tangible link between pathophysiological perturbations and age-related health outcomes.

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.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants

Objective.Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from>700extremely preterm infants to identify physiologic features that predict respiratory outcomes.Approach. We calculated a subset of 33 HCTSA features on>7 M 10 min windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on>3500HCTSA algorithms. We hypothesized that the best HCTSA algorithms would compare favorably to optimal PreVent physiologic predictor IH90_DPE (duration per event of intermittent hypoxemia events below 90%).Main Results.The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850).Significance. These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

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

BACKGROUND

Sinus 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).

HYPOTHESIS

Beat-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.

ANIMALS

Three 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).

METHODS

Heart 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.

RESULTS

Confusion 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).

CONCLUSIONS

Number 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.

Blood pressure fragmentation as a new measure of blood pressure variability: association with predictors of cardiac surgery outcomes

Background: Fluctuations in beat-to-beat blood pressure variability (BPV) encode untapped information of clinical utility. A need exists for developing new methods to quantify the dynamical properties of these fluctuations beyond their mean and variance. Objectives: Introduction of a new beat-to-beat BPV measure, termed blood pressure fragmentation (BPF), and testing of whether increased preoperative BPF is associated with (i) older age; (ii) higher cardiac surgical risk, assessed using the Society of Thoracic Surgeons' (STS) Risk of Morbidity and Mortality index and the European System for Cardiac Operative Risk Evaluation Score (EuroSCORE II); and (iii) longer ICU length of stay (LOS) following cardiac surgery. The secondary objective was to use standard BPV measures, specifically, mean, SD, coefficient of variation (CV), average real variability (ARV), as well a short-term scaling index, the detrended fluctuation analysis (DFA) ⍺1 exponent, in the same type of analyses to compare the results with those obtained using BPF. Methods: Consecutive sample of 497 adult patients (72% male; age, median [inter-quartile range]: 67 [59-75] years) undergoing cardiac surgery with cardiopulmonary bypass. Fragmentation, standard BPV and DFA ⍺1 measures were derived from preoperative systolic blood pressure (SBP) time series obtained from radial artery recordings. Results: Increased preoperative systolic BPF was associated with older age, higher STS Risk of Morbidity and Mortality and EuroSCORE II values, and longer ICU LOS in all models. Specifically, a one-SD increase in systolic BPF (9%) was associated with a 26% (13%-40%) higher likelihood of longer ICU LOS (>2 days). Among the other measures, only ARV and DFA ⍺1 tended to be associated with longer ICU LOS. However, the associations did not reach significance in the most adjusted models. Conclusion: Preoperative BPF was significantly associated with preoperative predictors of cardiac surgical outcomes as well as with ICU LOS. Our findings encourage future studies of preoperative BPF for assessment of health status and risk stratification of surgical and non-surgical patients.

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

INTRODUCTION

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.

METHODS

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.

RESULTS

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).

DISCUSSION

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.

Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants

Objective

Highly comparative time series analysis (HCTSA) is a novel approach involving massive feature extraction using publicly available code from many disciplines. The Prematurity-Related Ventilatory Control (Pre-Vent) observational multicenter prospective study collected bedside monitor data from > 700 extremely preterm infants to identify physiologic features that predict respiratory outcomes. We calculated a subset of 33 HCTSA features on > 7M 10-minute windows of oxygen saturation (SPO2) and heart rate (HR) from the Pre-Vent cohort to quantify predictive performance. This subset included representatives previously identified using unsupervised clustering on > 3500 HCTSA algorithms. Performance of each feature was measured by individual area under the receiver operating curve (AUC) at various days of life and binary respiratory outcomes. These were compared to optimal PreVent physiologic predictor IH90 DPE, the duration per event of intermittent hypoxemia events with threshold of 90%.

Main Results

The top HCTSA features were from a cluster of algorithms associated with the autocorrelation of SPO2 time series and identified low frequency patterns of desaturation as high risk. These features had comparable performance to and were highly correlated with IH90_DPE but perhaps measure the physiologic status of an infant in a more robust way that warrants further investigation. The top HR HCTSA features were symbolic transformation measures that had previously been identified as strong predictors of neonatal mortality. HR metrics were only important predictors at early days of life which was likely due to the larger proportion of infants whose outcome was death by any cause. A simple HCTSA model using 3 top features outperformed IH90_DPE at day of life 7 (.778 versus .729) but was essentially equivalent at day of life 28 (.849 versus .850). These results validated the utility of a representative HCTSA approach but also provides additional evidence supporting IH90_DPE as an optimal predictor of respiratory outcomes.

Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms

Aging is a complex biological process with multifactorial nature underlined by genetic, environmental, and social factors. In the present paper, we review several mechanisms of aging and the pre-clinically and clinically studied anti-aging therapies. Variability characterizes biological processes from the genome to cellular organelles, biochemical processes, and whole organs' function. Aging is associated with alterations in the degrees of variability and complexity of systems. The constrained disorder principle defines living organisms based on their inherent disorder within arbitrary boundaries and defines aging as having a lower variability or moving outside the boundaries of variability. We focus on associations between variability and hallmarks of aging and discuss the roles of disorder and variability of systems in the pathogenesis of aging. The paper presents the concept of implementing the constrained disease principle-based second-generation artificial intelligence systems for improving anti-aging modalities. The platform uses constrained noise to enhance systems' efficiency and slow the aging process. Described is the potential use of second-generation artificial intelligence systems in patients with chronic disease and its implications for the aged population.

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.

Evaluation of non-linear heart rate variability using multi-scale multi-fractal detrended fluctuation analysis in mice: Roles of the autonomic nervous system and sinoatrial node

Nonlinear analyses of heart rate variability (HRV) can be used to quantify the unpredictability, fractal properties and complexity of heart rate. Fractality and its analysis provides valuable information about cardiovascular health. Multi-Scale Multi-Fractal Detrended Fluctuation Analysis (MSMFDFA) is a complexity-based algorithm that can be used to quantify the multi-fractal dynamics of the HRV time series through investigating characteristic exponents at different time scales. This method is applicable to short time series and it is robust to noise and nonstationarity. We have used MSMFDFA, which enables assessment of HRV in the frequency ranges encompassing the very-low frequency and ultra-low frequency bands, to jointly assess multi-scale and multi-fractal dynamics of HRV signals obtained from telemetric ECG recordings in wildtype mice at baseline and after autonomic nervous system (ANS) blockade, from electrograms recorded from isolated atrial preparations and from spontaneous action potential recordings in isolated sinoatrial node myocytes. Data demonstrate that the fractal profile of the intrinsic heart rate is significantly different from the baseline heart rate in vivo, and it is also altered after ANS blockade at specific scales and fractal order domains. For beating rate in isolated atrial preparations and intrinsic heart rate in vivo, the average fractal structure of the HRV increased and multi-fractality strength decreased. These data demonstrate that fractal properties of the HRV depend on both ANS activity and intrinsic sinoatrial node function and that assessing multi-fractality at different time scales is an effective approach for HRV assessment.

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

Background

Dysautonomia 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.

Results

Phase 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).

Conclusions

The 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.

Research on Cross-Contrast Neural Network Based Intelligent Painting: Taking Oil Painting Language Classification as an Example

With the continuous fermentation of the thought of intelligence, artificial intelligence has extended its tentacles into the field of artistic creation and has begun to try intelligent creation. Painting creation based on artificial intelligence is called “intelligent painting.” For oil paintings, the computational language is a relatively complicated description. How to correctly identify the computational language of oil paintings is essential for establishing a large oil painting database. This paper constructs a meaningful learning similarity measure and multiclassification model based on the CCNN model to realize the classification of oil painting language. A cropped CNN model is used to extract language features, and on this basis, oil painting works are cross-compared and multiclassified. This method realizes the classification of oil painting language and the corresponding painter and achieves superior accuracy. This paper constructs a data classification method based on small samples, measures similarity through cross-comparison, and provides a measuring approach for classifying the language of oil paintings. The CCNN model proposed combines the best classification results of oil painting language, which improves the accuracy of oil painting language classification. Moreover, it further enriches the methods of oil painting language classification and image recognition under computational intelligence.

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 6 h 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 fragmentation, a novel approach in heart rate variability analysis, is altered in rats 4 and 12 weeks after myocardial infarction

An approach recently proposed to assess ultra-rapid patterns of heart rate variability, namely heart rate fragmentation (HRF), is increased in aging and coronary disease. We aimed to evaluate and to correlate HRF with cardiac functional parameters in a rat model of myocardial infarction (MI). Wistar rats were submitted to MI (n = 18) or sham operation (n = 20), and after 4 or 12 weeks, their arterial pressure was recorded. Subsequently, cardiac function was evaluated by echocardiography. From pulse interval series, HRF patterns with zero, one, two, or three inflection points were estimated, as well as the total percentage of inflection points (PIP). Cardiac function was reduced in MI rats. Ejection fraction was smaller 4 (28 ± 3 vs 68 ± 2%, p < 0.0001) and 12 weeks after MI (38 ± 3 vs 70 ± 3%, p < 0.0001). Fractional shortening was also smaller 4 (13 ± 2 vs 41 ± 2%, p < 0.0001) and 12 weeks after MI (20 ± 2 vs 41 ± 3%, p < 0.0001). PIP was increased in MI rats 4 (74 ± 2 vs 69 ± 1%, p = 0.03) and 12 weeks after surgery (70 ± 2 vs 63 ± 1%, p = 0.02). We found a significant negative correlation between cardiac functional parameters and HRF at both 4 and 12 weeks after MI. These findings reveal that MI increases HRF, reinforcing the importance of this approach to explore pathophysiological conditions. Evaluation of heart rate fragmentation (HRF) in a rat model of myocardial infarction (MI). Wistar rats were submitted to MI (n = 18) or sham operation (n = 20), and after 4 or 12 weeks, their arterial pressure was recorded. Cardiac function was evaluated by echocardiography. From pulse interval series, HRF patterns with zero (W0), one (W1), two (W3), or three (W3) inflection points were estimated, as well as the total percentage of inflection points (PIP). Cardiac function was reduced, while PIP was increased in all MI rats. Fluent patterns (W0 and W1) decreased in MI rats after 12 weeks. Altogether, the findings reveal that MI increases HRF, reinforcing the potential of this approach to explore pathophysiological conditions.

Fetal Heart Rate Fragmentation

Intrapartum fetal monitoring's primary goal is to avoid adverse perinatal outcomes related to hypoxia/acidosis without increasing unnecessary interventions. Recently, a set of indices were proposed as new biomarkers to analyze heart rate (HR), termed HR fragmentation (HRF). In this work, the HRF indices were applied to intrapartum fetal heart rate (FHR) traces to evaluate fetal acidemia. The fragmentation method produces four indices: PIP-Percentage of inflection points; IALS-Inverse of the average length of acceleration/deceleration segments; PSS-Percentage of short segments; PAS-Percentage of alternating segments. On the other hand, the symbolic approach studied the existence of different patterns of length four. We applied the measures to 246 selected FHR recordings sampled at 4 and 2 Hz, where 39 presented umbilical artery's pH ≤ 7.15. When applied to the 4 Hz FHR, the PIP, IASL, and PSS showed significantly higher values in the traces from acidemic fetuses. In comparison, the percentage of "words"W 1 h andW 2 s showed lower values for those traces. Furthermore, when using the 2 Hz, only IASL, W 0, andW 2 m achieved significant differences between traces from both acidemic and normal fetuses. Notwithstanding, the ideal sampling frequency is yet to be established. The fragmentation indices correlated with Sisporto variability measures, especially short-term variability. Accordingly, the fragmentation indices seem to be able to detect pathological patterns in FHR tracings. These indices have the advantage of being suitable and straightforward to apply in real-time analysis. Future studies should combine these indexes with others used successfully to detect fetal hypoxia, improving the power of discrimination in a larger dataset.

Prediction of Cognitive Decline Using Heart Rate Fragmentation Analysis: The Multi-Ethnic Study of Atherosclerosis

Background: Heart rate fragmentation (HRF), a new non-invasive metric quantifying cardiac neuroautonomic function, is associated with increasing age and cardiovascular disease. Since these are risk factors for cognitive decline and dementia, in the Multi-Ethnic Study of Atherosclerosis (MESA), we investigated whether disrupted cardiac neuroautonomic function, evidenced by increased HRF, would be associated with worse cognitive function assessed concurrently and at a later examination, and with greater cognitive decline. Methods: HRF was derived from the ECG channel of the polysomnographic recordings obtained in an ancillary study (n = 1,897) conducted in conjunction with MESA exam 5 (2010-2012). Cognitive function was assessed at exam 5 and 6.4 ± 0.5 years later at exam 6 (2016-2018) with tests of global cognitive performance (the Cognitive Abilities Screening Instrument, CASI), processing speed (Digit Symbol Coding, DSC) and working memory (Digit Span). Multivariable regression models were used to quantify the associations between HRF indices and cognitive scores. Results: The participants' mean age was 68 ± 9 years (54% female). Higher HRF at baseline was independently associated with lower cognitive scores at both exams 5 and 6. Specifically, in cross-sectional analyses, a one-standard deviation (SD) (13.7%) increase in HRF was associated with a 0.51 (95% CI: 0.17-0.86) points reduction in CASI and a 1.12 (0.34-1.90) points reduction in DSC. Quantitatively similar effects were obtained in longitudinal analyses. A one-SD increase in HRF was associated with a 0.44 (0.03-0.86) and a 1.04 (0.28-1.81) points reduction in CASI and DSC from exams 5 to 6, respectively. HRF added predictive value to the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE-APOE-ε4) risk score and to models adjusted for serum concentration of NT-proBNP, an analyte associated with cognitive impairment and dementia. Conclusion: Increased HRF assessed during sleep was independently associated with diminished cognitive performance (concurrent and future) and with greater cognitive decline. These findings lend support to the links between cardiac neuroautonomic regulation and cognitive function. As a non-invasive, repeatable and inexpensive probe, HRF technology may be useful in monitoring cognitive status, predicting risk of dementia and assessing therapeutic interventions.

PyBioS: A freeware computer software for analysis of cardiovascular signals

BACKGROUND AND OBJECTIVE

Several software applications have been proposed in the past years as computational tools for assessing biomedical signals. Many of them are focused on heart rate variability series only, with their strengths and limitations depending on the necessity of the user and the scope of the application. Here, we introduce new software, named PyBioS, intended for the analysis of cardiovascular signals, even though any type of biomedical signal can be used. PyBioS has some functionalities that differentiate it from the other software.

METHODS

PyBioS was developed in Python language with an intuitive, user-friendly graphical user interface. The basic steps for using PyBioS comprise the opening or creation (simulation) of signals, their visualization, preprocessing and analysis. Currently, PyBioS has 8 preprocessing tools and 15 analysis methods, the later providing more than 50 metrics for analysis of the signals' dynamics.

RESULTS

The possibility to create simulated signals and save the preprocessed signals is a strength of PyBioS. Besides, the software allows batch processing of files, making the analysis of a large amount of data easy and fast. Finally, PyBioS has plenty of analysis methods implemented, with the focus on nonlinear and complexity analysis of signals and time series.

CONCLUSIONS

Although PyBioS is not intended to overcome all the necessities from users, it has useful functionalities that may be helpful in many situations. Moreover, PyBioS is continuously under improvement and several simulated signals, tools and analysis methods are still to be implemented. Also, a new module is being implemented on it to provide machine learning algorithms for classification and regression of data extracted from the biomedical signals.

Fragmented sinoatrial dynamics in the prediction of atrial fibrillation: the Multi-Ethnic Study of Atherosclerosis

Heart rate fragmentation (HRF), a marker of abnormal sinoatrial dynamics, was shown to be associated with incident cardiovascular events in the Multi-Ethnic Study of Atherosclerosis (MESA). Here, we test the hypothesis that HRF is also associated with incident atrial fibrillation (AF) in the MESA cohort of participants who underwent in-home polysomnography (PSG) and in two high-risk subgroups: those ≥70 yr taking antihypertensive medication and those with serum concentrations of NH₂-terminal prohormone B-type natriuretic peptide (NT-proBNP) >125 pg/ml (top quartile). Heart rate time series (n = 1,858) derived from the ECG channel of the PSG were analyzed using newly developed HRF metrics, traditional heart rate variability (HRV) indices and two widely used nonlinear measures. Eighty-three participants developed AF over a mean follow-up period of 3.83 ± 0.87 yr. A one-standard deviation increase in HRF was associated with a 31% (95% CI: 3-66%) increase in risk of incident AF, in Cox models adjusted for age, height, NT-proBNP, and frequent premature supraventricular complexes. Furthermore, HRF added value to the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)-AF models. Traditional HRV and nonlinear indices were not significantly associated with incident AF. In the two high-risk subgroups defined above, HRF was also significantly associated with incident AF in unadjusted and adjusted models. These findings support the translational utility of HRF metrics for short-term (∼4-yr) prediction of AF. In addition, they support broadening the concept of atrial remodeling to include electrodynamical remodeling, a term used to refer to pathophysiological alterations in sinus interbeat interval dynamics.NEW & NOTEWORTHY This study is the first demonstration that heart rate fragmentation (HRF), a marker of anomalous sinoatrial dynamics, is an independent predictor of atrial fibrillation (AF). Traditional measures of heart rate variability and two widely used nonlinear measures were not associated with incident AF in the Multi-Ethnic Study of Atherosclerosis. Fragmentation measures added value to the strongest contemporary predictors of AF, including ECG-derived parameters, coronary calcification score, serum concentrations of NH₂-terminal prohormone B-type natriuretic peptide, and supraventricular ectopy. The computational algorithms for quantification of HRF could be readily incorporated into wearable ECG monitoring devices.

Heart rate fragmentation gives novel insights into non-autonomic mechanisms governing beat-to-beat control of the heart's rhythm

To demonstrate how heart rate fragmentation gives novel insights into non-autonomic mechanisms of beat-to-beat variability in cycle length, and predicts survival of cardiology clinic patients, over and above traditional clinical risk factors and measures of heart rate variability.

Approach: We studied 2893 patients seen by cardiologists with clinical data including 24-hour Holter monitoring. Novel measures of heart rate fragmentation alongside canonical time and frequency domain measures of heart rate variability, as well as an existing local dynamics score were calculated. A proportional hazards model was utilized to relate the results to survival.

Main results: The novel heart rate fragmentation measures were validated and characterized with respect to the effects of age, ectopy and atrial fibrillation. Correlations between parameters were determined. Critically, heart rate fragmentation results could not be accounted for by undersampling respiratory sinus arrhythmia. Increased heart rate fragmentation was associated with poorer survival (p ≪ 0.01 in the univariate model). In multivariable analyses, increased heart rate fragmentation and more abnormal local dynamics (p 0.045), along with increased clinical risk factors (age (p ≪ 0.01), tobacco use (p ≪ 0.01) and history of heart failure (p 0.019)) and lower low- to high-frequency ratio (p 0.022) were all independent predictors of 2-year mortality.

Significance: Analysis of continuous ECG data with heart rate fragmentation indices yields information regarding non-autonomic control of beat-to-beat variability in cycle length that is independent of and additive to established parameters for investigating heart rate variability, and predicts mortality in concert with measures of local dynamics, frequency content of heart rate, and clinical risk factors.

Heart Rate Variability in Children and Adolescents with Cerebral Palsy-A Systematic Literature Review

Cardiac autonomic dysfunction has been reported in patients with cerebral palsy (CP). The aim of this study was to assess the existing literature on heart rate variability (HRV) in pediatric patients with CP and a special attention was paid to the compliance of the studies with the current HRV assessment and interpretation guidelines. A systematic review was performed in PubMed, Web of Science, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases searched for English language publications from 1996 to 2019 using Medical Subject Headings (MeSH) terms “heart rate variability” and “cerebral palsy” in conjunction with additional inclusion criteria: studies limited to humans in the age range of 0–18 years and empirical investigations. Out of 47 studies, 12 were included in the review. Pediatric patients with CP presented a significantly higher resting heart rate and reduced HRV, different autonomic responses to movement stimuli compared to children with normal development, but also reduced HRV parameters in the children dependent on adult assistance for mobility compared to those generally independent. None of the included studies contained the necessary details concerning RR intervals acquisition and HRV measurements as recommended by the guidelines. Authors of HRV studies should follow the methodological guidelines and recommendations on HRV measurement, because such an approach may allow a direct comparison of their results.

Complexity of Cardiac Autonomic Modulation in Diabetes Mellitus: A New Technique to Perceive Autonomic Dysfunction

Backgound and aims. In this study we analyzed heart rate variability (HRV) via chaotic global techniques so as to discriminate diabetics from control subjects. Matherial and method. Chaotic global analysis of the RR-intervals from the electrocardiogram and preprocessing adjustments were undertaken. The effect of varying two parameters to adjust the Multi-Taper Method (MTM) power spectrum were evaluated. Then, cubic spline interpolations from 1Hz to 13Hz were applied whilst the spectral parameters were fixed. Precisely 1000 RR-intervals of data were recorded. Results. CFP1 and CFP3 are the only significant combinations of chaotic globals when the default standard conditions are enforced. MTM spectral adjustments and cubic spline interpolation are trivial at effecting the outcome between the two datasets. The most influencial constraint on the outcome is data length. Conclusion. Chaotic global analysis was offered as a reliable, low-cost and robust technique to detect autonomic dysfunction in subjects with diabetes mellitus.

The Pulse-Respiration Quotient: A Powerful but Untapped Parameter for Modern Studies About Human Physiology and Pathophysiology

A specific and unique aspect of cardiorespiratory activity can be captured by dividing the heart rate (HR) by the respiration rate (RR), giving the pulse-respiration quotient (PRQ = HR/RR). In this review article, we summarize the main findings of studies using and investigating the PRQ. We describe why the PRQ is a powerful parameter that captures complex regulatory states of the cardiorespiratory system, and we highlight the need to re-introduce the use of this parameter into modern studies about human physiology and pathophysiology. In particular, we show that the PRQ (i) changes during human development, (ii) is time-dependent (ultradian, circadian, and infradian rhythms), (iii) shows specific patterns during sleep, (iv) changes with physical activity and body posture, (v) is linked with psychophysical and cognitive activity, (vi) is sex-dependent, and (vii) is determined by the individual physiological constitution. Furthermore, we discuss the medical aspects of the PRQ in terms of applications for disease classification and monitoring. Finally, we explain why there should be a revival in the use of the PRQ for basic research about human physiology and for applications in medicine, and we give recommendations for the use of the PRQ in studies and medical applications.

Heart rate fragmentation: using cardiac pacemaker dynamics to probe the pace of biological aging

This perspectives article discusses the use of a novel set of dynamical biomarkers in the assessment of biological versus chronological age. The basis for this development is a recently delineated property of altered sinoatrial pacemaker-neuroautonomic function, termed heart rate fragmentation (HRF). Fragmented rhythms manifest as an increase in the density of changes in heart rate acceleration sign, not mechanistically explicable by physiological cardiac vagal tone modulation. We reported that HRF increased monotonically with cross-sectional age and that HRF measures, but not conventional heart rate variability metrics, were significantly associated with major incident cardiovascular events in the Multi-Ethnic Study of Atherosclerosis (MESA). Furthermore, HRF measures added value to both Framingham and MESA cardiovascular risk indices. Here, we propose that interventions that fundamentally slow or reverse the pace of biological aging, via system-wide effects, should be associated with a decrease in the degree of HRF and possibly with a reemergence of the nonfragmented ("fluent") patterns associated with more youthful heart rate dynamics.

Pitfalls of assessment of autonomic function by heart rate variability

Although analysis of heart rate variability is widely used for the assessment of autonomic function, its fundamental framework linking low-frequency and high-frequency components of heart rate variability with sympathetic and parasympathetic autonomic divisions has developed in the 1980s. This simplified framework is no longer able to deal with much evidence about heart rate variability accumulated over the past half-century. This review addresses the pitfalls caused by the old framework and discusses the points that need attention in autonomic assessment by heart rate variability.

A refined method of quantifying deceleration capacity index for heart rate variability analysis

Background

Phase-rectified signal averaging (PRSA) was often applied to assess the cardiac vagal modulation. Despite its broad use, this method suffers from the confounding effects of anomalous variants of sinus rhythm. This study aimed to improve the original PRSA method in deceleration capacity (DC) quantification.

Methods

The refined deceleration capacity (DC_ref) was calculated by excluding from non-vagally mediated abnormal variants of sinus rhythms. Holter recordings from 202 healthy subjects and 51 patients with end-stage renal disease (ESRD) have been used for validity. The DC_ref was compared to original DC (DC_org) by the area under receiver operating characteristic curve.

Results

Experimental results demonstrate that the original and refined DCs calculated from 24-h, 2-h, and 30-min Holter recordings are significantly lower in patients with ESRD than those in the healthy group. In receiver operating characteristic curve analysis, the DC_ref provides better performance than the DC_org in distinguishing between the patients with ESRD and healthy control subjects. Furthermore, the refined PRSA technique enhances the low frequency and attenuates high frequency components for spectral analysis in ESRD patients.

Conclusions

The DC_ref appears to reduce the influence of non-vagally mediated abnormal variants of sinus rhythm and highlighting the pathological influence. DC_ref, especially assessed from short-term electrocardiography recordings, may be complementary to existing autonomic function assessment, risk stratification, and efficacy prediction strategies.

Heart Rate Fragmentation as a Novel Biomarker of Adverse Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis

Background: A major objective of precision medicine is the elucidation of non-invasive biomarkers of cardiovascular (CV) risk. Recently, we introduced a new dynamical marker of sino-atrial instability, termed heart rate fragmentation (HRF), which outperformed traditional and nonlinear heart rate variability metrics in separating ostensibly healthy subjects from patients with coronary artery disease. Accordingly, we hypothesized that HRF may be a dynamical biomarker of adverse cardiovascular events (CVEs). Methods: This study employed data from a cohort of participants in the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of sub-clinical heart disease. Interbeat interval time series (n = 1963), derived from the electrocardiographic channel of the polysomnogram study, were analyzed using the newly introduced metrics of fragmentation, as well as traditional heart rate variability (HRV) indices and the short-term detrended fluctuation analysis exponent. Cox regression analysis was used to assess the association between HR dynamic indices and CV outcomes in unadjusted and adjusted models. Results: The mean (± SD) follow-up time was 2.97 ± 0.63 years. In adjusted models, higher fragmentation was significantly associated with incident CVEs (number of events; hazard ratio [95% confidence interval]: n = 72, 1.43 [1.16-1.76]) and CV death (n = 21; 1.65 [1.15-2.36]). The traditional HRV and the fractal indices were not associated with CVEs or CV death. The most discriminatory fragmentation indices added significant value to Framingham and MESA CV risk indices in all analyses. Conclusion: Our findings show that HRF has promise as a non-invasive, automatable biomarker of CV risk. The basic mechanisms underlying fragmentation remain to be delineated. Its association with incident outcomes raises the possibility of connections to degenerative changes in the multisystem network controlling SAN function.

Cardiovascular assessment of supportive doctor-patient communication using multi-scale and multi-lag analysis of heartbeat dynamics

Emphatic doctor-patient communication has been associated with improved psycho-physiological well-being involving cardiovascular and neuroendocrine responses. Nevertheless, a comprehensive assessment of heartbeat linear and nonlinear dynamics throughout the communication of a life-threatening disease has not been performed yet. To this extent, we studied linear heartbeat dynamics through the extraction of time-frequency domain measurements, as well as heartbeat nonlinear and complex dynamics through novel approaches to compute multi-scale and multi-lag series analyses: namely, the multi-scale distribution entropy and lagged Poincaré plot symbolic analysis. Heart rate variability series were recorded from 54 healthy female subjects who were blind to the aim of the experiment. Participants were randomly assigned into two groups: 27 subjects watched a video where an oncologist discloses the diagnosis of a cancer metastasis to a patient, whereas the remaining 27 watched the same video including four additional supportive comments by the clinician. Considering differences between the beginning and the end of each communication video, results from non-parametric Wilcoxon tests demonstrated that, at a group level, significant differences occurred in heartbeat linear and nonlinear dynamics, with lower complexity during nonsupportive communication. Furthermore, a support vector machine algorithm, validated using a leave-one-subject-out procedure, was able to discern the supportive experience at a single-subject level with an accuracy of 83.33% when nonlinear features were considered, dropping to 51.85% when using standard HRV features only. In conclusion, heartbeat nonlinear and complex dynamics can be a viable tool for the psycho-physiological evaluation of supportive doctor-patient communication. Graphical Abstract Scheme of the three main stages of the study: signal acquisition during doctor-patient communication, ECG signal processing and pattern recognition results.

Changes in the Complexity of Heart Rate Variability with Exercise Training Measured by Multiscale Entropy-Based Measurements

Quantifying complexity from heart rate variability (HRV) series is a challenging task, and multiscale entropy (MSE), along with its variants, has been demonstrated to be one of the most robust approaches to achieve this goal. Although physical training is known to be beneficial, there is little information about the long-term complexity changes induced by the physical conditioning. The present study aimed to quantify the changes in physiological complexity elicited by physical training through multiscale entropy-based complexity measurements. Rats were subject to a protocol of medium intensity training ( n = 13 ) or a sedentary protocol ( n = 12 ). One-hour HRV series were obtained from all conscious rats five days after the experimental protocol. We estimated MSE, multiscale dispersion entropy (MDE) and multiscale SDiff q from HRV series. Multiscale SDiff q is a recent approach that accounts for entropy differences between a given time series and its shuffled dynamics. From SDiff q , three attributes (q-attributes) were derived, namely SDiff q m a x , q m a x and q z e r o . MSE, MDE and multiscale q-attributes presented similar profiles, except for SDiff q m a x . q m a x showed significant differences between trained and sedentary groups on Time Scales 6 to 20. Results suggest that physical training increases the system complexity and that multiscale q-attributes provide valuable information about the physiological complexity.