Joseph E. Mietus

Spectral characteristics of heart rate variability before and during postural tilt. Relations to aging and risk of syncope.

Fourier analysis of heart rate (HR) may be used to characterize overall HR variability as well as low- and high-frequency components attributable to sympathetic and vagal influences, respectively. We analyzed HR spectral characteristics of 12 healthy young (18-35 years) and 10 healthy old (71-94 years) subjects before and during 60 degrees head-up tilt. Total spectral power in the 0.01-0.40-Hz frequency range and low-frequency (0.06-0.10 Hz) and high-frequency (0.15-0.40 Hz) components of the HR power spectrum were significantly lower in old than in young subjects in supine and upright positions. To characterize and compare overall HR variability in young and old subjects, we computed the regression lines relating the log amplitude to the log frequency of the supine HR spectra (l/fx plots). The regression lines for old subjects were lower and steeper (mean slope, -0.78 [5%, 95% confidence limits (CL), -0.73, -0.83]) than in young (mean slope, -0.67 [CL, -0.62, -0.72]), indicating not only reduced overall spectral amplitude but also relatively greater attenuation of high-frequency HR components in the old subjects. This finding illustrates a novel way to quantify the loss of autonomic influences on HR regulation as a function of age. During postural tilt, HR variability was unchanged in the old subjects. For the entire group of young subjects, total HR variability increased during tilt. Six young subjects developed vasovagal syncope during tilt, enabling us to examine differences in the HR spectra of these subjects while they were asymptomatic before syncope.(ABSTRACT TRUNCATED AT 250 WORDS)

Predicting Survival in Heart Failure Case and Control Subjects by Use of Fully Automated Methods for Deriving Nonlinear and Conventional Indices of Heart Rate Dynamics

BACKGROUND Despite much recent interest in quantification of heart rate variability (HRV), the prognostic value of conventional measures of HRV and of newer indices based on nonlinear dynamics is not universally accepted. METHODS AND RESULTS We have designed algorithms for analyzing ambulatory ECG recordings and measuring HRV without human intervention, using robust methods for obtaining time-domain measures (mean and SD of heart rate), frequency-domain measures (power in the bands of 0.001 to 0.01 Hz [VLF], 0.01 to 0.15 Hz [LF], and 0.15 to 0.5 Hz [HF] and total spectral power [TP] over all three of these bands), and measures based on nonlinear dynamics (approximate entropy [ApEn], a measure of complexity, and detrended fluctuation analysis [DFA], a measure of long-term correlations). The study population consisted of chronic congestive heart failure (CHF) case patients and sex- and age-matched control subjects in the Framingham Heart Study. After exclusion of technically inadequate studies and those with atrial fibrillation, we used these algorithms to study HRV in 2-hour ambulatory ECG recordings of 69 participants (mean age, 71.7+/-8.1 years). By use of separate Cox proportional-hazards models, the conventional measures SD (P<.01), LF (P<.01), VLF (P<.05), and TP (P<.01) and the nonlinear measure DFA (P<.05) were predictors of survival over a mean follow-up period of 1.9 years; other measures, including ApEn (P>.3), were not. In multivariable models, DFA was of borderline predictive significance (P=.06) after adjustment for the diagnosis of CHF and SD. CONCLUSIONS These results demonstrate that HRV analysis of ambulatory ECG recordings based on fully automated methods can have prognostic value in a population-based study and that nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.

Fractal mechanisms and heart rate dynamics: Long-range correlations and their breakdown with disease*

Under healthy conditions, the normal cardiac (sinus) interbeat interval fluctuates in a complex manner. Quantitative analysis using techniques adapted from statistical physics reveals the presence of long-range power-law correlations extending over thousands of heartbeats. This scale-invariant (fractal) behavior suggests that the regulatory system generating these fluctuations is operating far from equilibrium. In contrast, it is found that for subjects at high risk of sudden death (e.g., congestive heart failure patients), these long-range correlations break down. Application of fractal scaling analysis and related techniques provides new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as motivating development of novel physiologic models of systems that appear to be heterodynamic rather than homeostatic.

Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects

AbstractWe sought to quantify the fractal scaling properties of human respiratory dynamics and determine whether they are altered with healthy aging and gender. Continuous respiratory datasets (obtained by inductive plethysmography) were collected from 40 healthy adults (10 young men, 10 young women, 10 elderly men, and 10 elderly women) during 120 min of spontaneous breathing. The interbreath interval (IBI) time series were extracted by a new algorithm and fractal scaling exponents that quantify power-law correlations were computed using detrended fluctuation analysis. Under supine, resting, and spontaneous breathing conditions, both healthy young and elderly subjects had scaling exponents for the IBI time series that indicate long-range (fractal) correlations across multiple time scales. Furthermore, the scaling exponents (mean ± SD) for the IBI time series were significantly (p < 0.03) lower (indicating decreased correlations) in the healthy elderly male 0.60 ± 0.08) compared to the young male (0.68 ± 0.07), young female (0.70 ± 0.07), and elderly female (0.67 ± 0.06) subjects. These results provide evidence for fractal organization in physiologic human breathing cycle dynamics, and for their degradation in elderly men. These findings may have implications for modeling integrated respiratory control mechanisms, quantifying their changes in aging or disease, and assessing the outcome of interventions aimed toward restoring normal physiologic respiratory dynamics.

Nonlinear dynamics in sudden cardiac death syndrome: heartrate oscillations and bifurcations.

Patients at high risk of sudden cardiac death show evidence of nonlinear heartrate dynamics, including abrupt spectral changes (bifurcations) and sustained low frequency (.01–.04 Hz) oscillations in heartrate.

Exaggerated heart rate oscillations during two meditation techniques

We report extremely prominent heart rate oscillations associated with slow breathing during specific traditional forms of Chinese Chi and Kundalini Yoga meditation techniques in healthy young adults. We applied both spectral analysis and a novel analytic technique based on the Hilbert transform to quantify these heart rate dynamics. The amplitude of these oscillations during meditation was significantly greater than in the pre-meditation control state and also in three non-meditation control groups: i) elite athletes during sleep, ii) healthy young adults during metronomic breathing, and iii) healthy young adults during spontaneous nocturnal breathing. This finding, along with the marked variability of the beat-to-beat heart rate dynamics during such profound meditative states, challenges the notion of meditation as only an autonomically quiescent state.

The pNNx files: re-examining a widely used heart rate variability measure

Objective: To re-examine the standard pNN50 heart rate variability (HRV) statistic by determining how other thresholds compare with the commonly adopted 50 ms threshold in distinguishing physiological and pathological groups. Design: Retrospective analysis of Holter monitor databases. Subjects: Comparison of HRV data between 72 healthy subjects and 43 with congestive heart failure (CHF); between sleeping and waking states in the 72 healthy subjects; and between 20 young and 20 healthy elderly subjects. Main outcome measures: Probability values for discriminating between groups using a family of pNN values ranging from pNN4 to pNN100. Results: For all three comparisons, pNN values substantially less than 50 ms consistently provided better separation between groups. For the normal versus CHF groups, p < 10−13 for pNN12 versus p < 10−4 for pNN50; for the sleeping versus awake groups, p < 10−21 for pNN12 versus p < 10−10 for pNN50; and for the young versus elderly groups, p < 10−6 for pNN28 versus p < 10−4 for pNN50. In addition, for the subgroups of elderly healthy subjects versus younger patients with CHF, p < 0.007 for pNN20 versus p < 0.17 for pNN50; and for the subgroup of New York Heart Association functional class I–II CHF versus class III–IV, p < 0.04 for pNN10 versus p < 0.13 for pNN50. Conclusions: pNN50 is only one member of a general pNNx family of HRV statistics. Enhanced discrimination between a variety of normal and pathological conditions is obtained by using pNN thresholds as low as 20 ms or less rather than the standard 50 ms threshold.

Conventional heart rate variability analysis of ambulatory electrocardiographiic recording fails to predict imminent ventricular fibrillation

OBJECTIVES The purpose of this report was to study heart rate variability in Holter recordings of patients who experienced ventricular fibrillation during the recording. BACKGROUND Decreased heart rate variability is recognized as a long-term predictor of overall and arrhythmic death after myocardial infarction. It was therefore postulated that heart rate variability would be lowest when measured immediately before ventricular fibrillation. METHODS Conventional indexes of heart rate variability were calculated from Holter recordings of 24 patients with structural heart disease who had ventricular fibrillation during monitoring. The control group consisted of 19 patients with coronary artery disease, of comparable age and left ventricular ejection fraction, who had nonsustained ventricular tachycardia but no ventricular fibrillation. RESULTS Heart rate variability did not differ between the two groups, and no consistent trends in heart rate variability were observed before ventricular fibrillation occurred. CONCLUSIONS Although conventional heart rate variability is an independent long-term predictor of adverse outcome after myocardial infarction, its clinical utility as a short-term predictor of life-threatening arrhythmias remains to be elucidated.

Non-equilibrium dynamics as an indispensable characteristic of a healthy biological system

Healthy systems in physiology and medicine are remarkable for their structural variability and dynamical complexity. The concept of fractal growth and form offers novel approaches to understanding morphogenesis and function from the level of the gene to the organism. For example, scale-invariance and long-range power-law correlations are features of non-coding DNA sequences as well as of healthy heartbeat dynamics. For cardiac regulation, perturbation of the control mechanisms by disease or aging may lead to a breakdown of these long-range correlations that normally extend over thousands of heartbeats. Quantification of such long-range scaling alterations are providing new approaches to problems ranging from molecular evolution to monitoring patients at high risk of sudden death.We briefly review recent work from our laboratory concerning the application of fractals to two apparently unrelated problems: DNA organization and beat-to-beat heart rate variability. We show how the measurement of long-range power-law correlations may provide new understanding of nucleotide organization as well as of the complex fluctuations of the heartbeat under normal and pathologic conditions.

Obstructive Sleep Apnea Alters Sleep Stage Transition Dynamics

Introduction Enhanced characterization of sleep architecture, compared with routine polysomnographic metrics such as stage percentages and sleep efficiency, may improve the predictive phenotyping of fragmented sleep. One approach involves using stage transition analysis to characterize sleep continuity. Methods and Principal Findings We analyzed hypnograms from Sleep Heart Health Study (SHHS) participants using the following stage designations: wake after sleep onset (WASO), non-rapid eye movement (NREM) sleep, and REM sleep. We show that individual patient hypnograms contain insufficient number of bouts to adequately describe the transition kinetics, necessitating pooling of data. We compared a control group of individuals free of medications, obstructive sleep apnea (OSA), medical co-morbidities, or sleepiness (n = 374) with mild (n = 496) or severe OSA (n = 338). WASO, REM sleep, and NREM sleep bout durations exhibited multi-exponential temporal dynamics. The presence of OSA accelerated the “decay” rate of NREM and REM sleep bouts, resulting in instability manifesting as shorter bouts and increased number of stage transitions. For WASO bouts, previously attributed to a power law process, a multi-exponential decay described the data well. Simulations demonstrated that a multi-exponential process can mimic a power law distribution. Conclusion and Significance OSA alters sleep architecture dynamics by decreasing the temporal stability of NREM and REM sleep bouts. Multi-exponential fitting is superior to routine mono-exponential fitting, and may thus provide improved predictive metrics of sleep continuity. However, because a single night of sleep contains insufficient transitions to characterize these dynamics, extended monitoring of sleep, probably at home, would be necessary for individualized clinical application.