We introduce a generalization of multiscale entropy (MSE) analysis. The method is termed MSE
, where the subscript denotes the moment used to coarse-grain a time series. MSE
, described previously, ...uses the mean value (first moment). Here, we focus on Formula: see text, which uses the second moment,
, the variance. Formula: see text quantifies the dynamics of the volatility (variance) of a signal over multiple time scales. We use the method to analyze the structure of heartbeat time series. We find that the dynamics of the volatility of heartbeat time series obtained from healthy young subjects is highly complex. Furthermore, we find that the multiscale complexity of the volatility, not only the multiscale complexity of the mean heart rate, degrades with aging and pathology. The "bursty" behavior of the dynamics may be related to intermittency in energy and information flows, as part of multiscale cycles of activation and recovery. Generalized MSE may also be useful in quantifying the dynamical properties of other physiologic and of non-physiologic time series.
Professional sleep societies have identified a need for strategic research in multiple areas that may benefit from access to and aggregation of large, multidimensional datasets. Technological ...advances provide opportunities to extract and analyze physiological signals and other biomedical information from datasets of unprecedented size, heterogeneity, and complexity. The National Institutes of Health has implemented a Big Data to Knowledge (BD2K) initiative that aims to develop and disseminate state of the art big data access tools and analytical methods. The National Sleep Research Resource (NSRR) is a new National Heart, Lung, and Blood Institute resource designed to provide big data resources to the sleep research community. The NSRR is a web-based data portal that aggregates, harmonizes, and organizes sleep and clinical data from thousands of individuals studied as part of cohort studies or clinical trials and provides the user a suite of tools to facilitate data exploration and data visualization. Each deidentified study record minimally includes the summary results of an overnight sleep study; annotation files with scored events; the raw physiological signals from the sleep record; and available clinical and physiological data. NSRR is designed to be interoperable with other public data resources such as the Biologic Specimen and Data Repository Information Coordinating Center Demographics (BioLINCC) data and analyzed with methods provided by the Research Resource for Complex Physiological Signals (PhysioNet). This article reviews the key objectives, challenges and operational solutions to addressing big data opportunities for sleep research in the context of the national sleep research agenda. It provides information to facilitate further interactions of the user community with NSRR, a community resource.
Short-term heart rate variability (HRV) is most commonly attributed to physiologic vagal tone modulation. However, with aging and cardiovascular disease, the emergence of high short-term HRV, ...consistent with the breakdown of the neuroautonomic-electrophysiologic control system, may confound traditional HRV analysis. An apparent dynamical signature of such anomalous short-term HRV is frequent changes in heart rate acceleration sign, defined here as heart rate fragmentation.
The aims were to: (1) introduce a set of metrics designed to probe the degree of sinus rhythm fragmentation; (2) test the hypothesis that the degree of fragmentation of heartbeat time series increases with the participants' age in a group of healthy subjects; (3) test the hypothesis that the heartbeat time series from patients with advanced coronary artery disease (CAD) are more fragmented than those from healthy subjects; and (4) compare the performance of the new fragmentation metrics with standard time and frequency domain measures of short-term HRV.
We analyzed annotated, open-access Holter recordings (University of Rochester Holter Warehouse) from healthy subjects and patients with CAD using these newly introduced metrics of heart rate fragmentation, as well as standard time and frequency domain indices of short-term HRV, detrended fluctuation analysis and sample entropy.
The degree of fragmentation of cardiac interbeat interval time series increased significantly as a function of age in the healthy population as well as in patients with CAD. Fragmentation was higher for the patients with CAD than the healthy subjects. Heart rate fragmentation metrics outperformed traditional short-term HRV indices, as well as two widely used nonlinear measures, sample entropy and detrended fluctuation analysis short-term exponent, in distinguishing healthy subjects and patients with CAD. The same level of discrimination was obtained from the analysis of normal-to-normal sinus (NN) and cardiac interbeat interval (RR) time series.
The fragmentation framework and accompanying metrics introduced here constitute a new way of assessing short-term HRV under free-running conditions, one which appears to overcome salient limitations of traditional HRV analysis. Fragmentation of sinus rhythm cadence may provide new dynamical biomarkers for probing the integrity of the neuroautonomic-electrophysiologic network controlling the heartbeat in health and disease.
There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic ...processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
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.
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 (
< 0.001) lower after sleep (before: 74 ± 12%, after: 67 ± 13%). Furthermore, the magnitude of the decrease significantly (
< 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.
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.
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) ⍺
exponent, in the same type of analyses to compare the results with those obtained using BPF.
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 ⍺
measures were derived from preoperative systolic blood pressure (SBP) time series obtained from radial artery recordings.
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 ⍺
tended to be associated with longer ICU LOS. However, the associations did not reach significance in the most adjusted models.
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.
We recently introduced the concept of heart rate fragmentation along with a set of metrics for its quantification. The term was coined to refer to an increase in the percentage of changes in heart ...rate acceleration sign, a dynamical marker of a type of anomalous variability. The effort was motivated by the observation that fragmentation, which is consistent with the breakdown of the neuroautonomic-electrophysiologic control system of the sino-atrial node, could confound traditional short-term analysis of heart rate variability.
The objectives of this study were to: (1) introduce a symbolic dynamical approach to the problem of quantifying heart rate fragmentation; (2) evaluate how the distribution of the different dynamical patterns ("words") varied with the participants' age in a group of healthy subjects and patients with coronary artery disease (CAD); and (3) quantify the differences in the fragmentation patterns between the two sample populations.
The symbolic dynamical method employed here was based on a ternary map of the increment NN interval time series and on the analysis of the relative frequency of symbolic sequences (words) with a pre-defined set of features. We analyzed annotated, open-access Holter databases of healthy subjects and patients with CAD, provided by the University of Rochester Telemetric and Holter ECG Warehouse (THEW).
The degree of fragmentation was significantly higher in older individuals than in their younger counterparts. However, the fragmentation patterns were different in the two sample populations. In healthy subjects, older age was significantly associated with a higher percentage of transitions from acceleration/deceleration to zero acceleration and
(termed "soft" inflection points). In patients with CAD, older age was also significantly associated with higher percentages of frank reversals in heart rate acceleration (transitions from acceleration to deceleration and
, termed "hard" inflection points). Compared to healthy subjects, patients with CAD had significantly higher percentages of soft and hard inflection points, an increased percentage of words with a high degree of fragmentation and a decreased percentage of words with a lower degree of fragmentation.
The symbolic dynamical method employed here was useful to probe the newly recognized property of heart rate fragmentation. The findings from these cross-sectional studies confirm that CAD and older age are associated with higher levels of heart rate fragmentation. Furthermore, fragmentation with healthy aging appears to be phenotypically different from fragmentation in the context of CAD.
According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, ...however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era.
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