Decreased neuroautonomic complexity in men during an acute major depressive episode: analysis of heart rate dynamics.

Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Increasing evidence supports the conceptual framework that complex variability is a marker of healthy, adaptive control mechanisms and that dynamical complexity decreases with aging and disease. We tested the hypothesis that heart rate (HR) dynamics in non-medicated, young to middle-aged males during an acute major depressive episode would exhibit lower complexity compared with healthy counterparts. We analyzed HR time series, a neuroautonomically regulated signal, during sleep, using the multiscale entropy method. Our results show that the complexity of the HR dynamics is significantly lower for depressed than for non-depressed subjects for the entire night (P<0.02) and combined sleep stages 1 and 2 (P<0.02). These findings raise the possibility of using the complexity of physiologic signals as the basis of novel dynamical biomarkers of depression.

Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance-based therapy.

Pathologic states are associated with a loss of dynamical complexity. Therefore, therapeutic interventions that increase physiologic complexity may enhance health status. Using multiscale entropy analysis, we show that the postural sway dynamics of healthy young and healthy elderly subjects are more complex than that of elderly subjects with a history of falls. Application of subsensory noise to the feet has been demonstrated to improve postural stability in the elderly. We next show that this therapy significantly increases the multiscale complexity of sway fluctuations in healthy elderly subjects. Quantification of changes in dynamical complexity of biologic variability may be the basis of a new approach to assessing risk and to predicting the efficacy of clinical interventions, including noise-based therapies.

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.

Multiscale entropy to distinguish physiologic and synthetic RR time series.

We address the challenge of distinguishing physiologic interbeat interval time series from those generated by synthetic algorithms via a newly developed multiscale entropy method. Traditional measures of time series complexity only quantify the degree of regularity on a single time scale. However, many physiologic variables, such as heart rate, fluctuate in a very complex manner and present correlations over multiple time scales. We have proposed a new method to calculate multiscale entropy from complex signals. In order to distinguish between physiologic and synthetic time series, we first applied the method to a learning set of RR time series derived from healthy subjects. We empirically established selected criteria characterizing the entropy dependence on scale factor for these datasets. We then applied this algorithm to the CinC 2002 test datasets. Using only the multiscale entropy method, we correctly classified 48 of 50 (96%) time series. In combination with Fourier spectral analysis, we correctly classified all time series.

Marked exacerbation of orthostatic intolerance after long- vs. short-duration spaceflight in veteran astronauts.

OBJECTIVE: The incidence of postflight orthostatic intolerance after short-duration spaceflight is about 20%. However, the incidence after long-duration spaceflight was unknown. The purpose of this study was to test the hypothesis that orthostatic intolerance is more severe after long-duration than after short-duration flight. METHODS: We performed tilt tests on six astronauts before and after long-duration (129-190 days) spaceflights and compared these data with data obtained during stand tests before and after previous short-duration missions. RESULTS: Five of the six astronauts studied became presyncopal during tilt testing after long-duration flights. Only one had become presyncopal during stand testing after short-duration flights. We also compared the long-duration flight tilt test data to tilt test data from 20 different astronauts who flew on the short-duration Shuttle missions that delivered and recovered the astronauts to and from the Mir Space Station. Five of these 20 astronauts became presyncopal on landing day. Heart rate responses to tilt were no different between astronauts on long-duration flights and astronauts on short-duration flights, but long-duration subjects had lower stroke volumes and cardiac outputs than short-duration presyncopal subjects, suggesting a possible decrease in cardiac contractile function. One subject had subnormal norepinephrine release with upright posture after the long flight but not after the short flight. Plasma volume losses were not greater after long flights. CONCLUSION: Long-duration spaceflight markedly increases orthostatic intolerance, probably with multiple contributing factors.

Noise effects on the complex patterns of abnormal heartbeats.

Patients at high risk for sudden death often exhibit complex heart rhythms in which abnormal heartbeats are interspersed with normal heartbeats. We analyze such a complex rhythm in a single patient over a 12-h period and show that the rhythm can be described by a theoretical model consisting of two interacting oscillators with stochastic elements. By varying the magnitude of the noise, we show that for an intermediate level of noise, the model gives best agreement with key statistical features of the dynamics.

Behavioral-independent features of complex heartbeat dynamics.

We test whether the complexity of the cardiac interbeat interval time series is simply a consequence of the wide range of scales characterizing human behavior, especially physical activity, by analyzing data taken from healthy adult subjects under three conditions with controls: (i) a "constant routine" protocol where physical activity and postural changes are kept to a minimum, (ii) sympathetic blockade, and (iii) parasympathetic blockade. We find that when fluctuations in physical activity and other behavioral modifiers are minimized, a remarkable level of complexity of heartbeat dynamics remains, while for neuroautonomic blockade the multifractal complexity decreases.

Magnitude and sign correlations in heartbeat fluctuations.

We propose an approach for analyzing signals with long-range correlations by decomposing the signal increment series into magnitude and sign series and analyzing their scaling properties. We show that signals with identical long-range correlations can exhibit different time organization for the magnitude and sign. We find that the magnitude series relates to the nonlinear properties of the original time series, while the sign series relates to the linear properties. We apply our approach to the heartbeat interval series and find that the magnitude series is long-range correlated, while the sign series is anticorrelated and that both magnitude and sign series may have clinical applications.

When human walking becomes random walking: fractal analysis and modeling of gait rhythm fluctuations.

We present a random walk, fractal analysis of the stride-to-stride fluctuations in the human gait rhythm. The gait of healthy young adults is scale-free with long-range correlations extending over hundreds of strides. This fractal scaling changes characteristically with maturation in children and older adults and becomes almost completely uncorrelated with certain neurologic diseases. Stochastic modeling of the gait rhythm dynamics, based on transitions between different "neural centers", reproduces distinctive statistical properties of the gait pattern. By tuning one model parameter, the hopping (transition) range, the model can describe alterations in gait dynamics from childhood to adulthood including a decrease in the correlation and volatility exponents with maturation.

ECG Wave-Maven: a self-assessment program for students and clinicians.

Proficiency in the interpretation of electrocardiograms (ECGs) is an essential skill for medical students, house officers, and attending physicians. However, resources to develop and upgrade the necessary high level of "ECG literacy" are limited. A small number of centers have attempted to address this challenge by developing "ECG of the week" internet sites. These resources are difficult to maintain and update, and many of them quickly become stagnant. We present "ECG Wave-Maven," an innovative web-based tutorial that overcomes these obstacles via a direct link to the hospital's extensive and increasing clinical ECG repository. By interfacing our educational tool to live data, we can greatly decrease the time and effort required from the time a practitioner notes an interesting case to its inclusion in the program. Users can opt to encounter the test cases sequentially or randomly, or by reviewing a list of questions or diagnoses, making this not just a quiz, but a basic educational reference. This tool may be useful in meeting the challenge of reducing serious medical errors related to ECG misinterpretation.

Holter monitoring: are two days better than one?

The optimal duration of Holter monitoring (HM) to minimize costs and maximize yield is unknown. In a retrospective review of 164 patients referred to a tertiary care center for evaluation with 2 days of HM, we found that 48 hours was not cost effective when compared with the traditional 24-hour period.

PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.

The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.

Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a disorder marked by loss of motoneurons. We hypothesized that subjects with ALS would have an altered gait rhythm, with an increase in both the magnitude of the stride-to-stride fluctuations and perturbations in the fluctuation dynamics. To test for this locomotor instability, we quantitatively compared the gait rhythm of subjects with ALS with that of normal controls and with that of subjects with Parkinson's disease (PD) and Huntington's disease (HD), pathologies of the basal ganglia. Subjects walked for 5 min at their usual pace wearing an ankle-worn recorder that enabled determination of the duration of each stride and of stride-to-stride fluctuations. We found that the gait of patients with ALS is less steady and more temporally disorganized compared with that of healthy controls. In addition, advanced ALS, HD, and PD were associated with certain common, as well as apparently distinct, features of altered stride dynamics. Thus stride-to-stride control of gait rhythm is apparently compromised with ALS. Moreover, a matrix of markers based on gait dynamics may be useful in characterizing certain pathologies of motor control and, possibly, in quantitatively monitoring disease progression and evaluating therapeutic interventions.

Oxygen desaturations triggered by partial seizures: implications for cardiopulmonary instability in epilepsy.

PURPOSE: The occurrence of hypoxemia in adults with partial seizures has not been systematically explored. Our aim was to study in detail the temporal dynamics of this specific type of ictal-associated hypoxemia. METHODS: During long-term video/EEG monitoring (LTM), patients underwent monitoring of oxygen saturation using a digital Spo2 (pulse oximeter) transducer. Six patients (nine seizures) were identified with oxygen desaturations after the onset of partial seizure activity. RESULTS: Complex partial seizures originated from both left and right temporal lobes. Mean seizure duration (+/-SD) was 73 +/- 18 s. Mean Spo2 desaturation duration was 76 +/- 19 s. The onset of oxygen desaturation followed seizure onset with a mean delay of 43 +/- 16 s. Mean (+/-SD) Spo2 nadir was 83 +/- 5% (range, 77-91%), occurring an average of 35 +/- 12 s after the onset of the desaturation. One seizure was associated with prolonged and recurrent Spo2 desaturations. CONCLUSIONS: Partial seizures may be associated with prominent oxygen desaturations. The comparable duration of each seizure and its subsequent desaturation suggests a close mechanistic (possibly causal) relation. Spo2 monitoring provides an added means for seizure detection that may increase LTM yield. These observations also raise the possibility that ictal ventilatory dysfunction could play a role in certain cases of sudden unexpected death in epilepsy in adults with partial seizures.

A new method to determine a fractal dimension of non-stationary biological time-serial data.

We devised a new analysis using quartile deviation of integrated and subtracted fluctuation, termed QIS-A, to determine a fractal dimension of non-stationary fluctuation. In the algorithm, computations of the quartile deviation, Q(n), of all integrated and subtracted fluctuations are repeated over all scales (n). The fractal scaling exponent is determined as a slope of the line relating log Q(n) to log n. Comparison of the QIS-A and a spectral analysis using 20 computer-simulated fractional Brownian motions demonstrates robustness of the QIS-A to non-stationary fluctuations.

Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction.

BACKGROUND: Preliminary data suggest that the analysis of R-R interval variability by fractal analysis methods may provide clinically useful information on patients with heart failure. The purpose of this study was to compare the prognostic power of new fractal and traditional measures of R-R interval variability as predictors of death after acute myocardial infarction. METHODS AND RESULTS: Time and frequency domain heart rate (HR) variability measures, along with short- and long-term correlation (fractal) properties of R-R intervals (exponents alpha(1) and alpha(2)) and power-law scaling of the power spectra (exponent beta), were assessed from 24-hour Holter recordings in 446 survivors of acute myocardial infarction with a depressed left ventricular function (ejection fraction

Decomposition of heartbeat time series: scaling analysis of the sign sequence.

The cardiac interbeat (RR) increment time series can be decomposed into two sub-sequences: a magnitude series and a sign series. The authors show that the sign sequence, a simple binary representation of the original RR series, retains fundamental scaling properties of the original series, is robust with respect to outliers, and may provide useful information about neuroautonomic control mechanisms.

PhysioNet: a research resource for studies of complex physiologic and biomedical signals.

PhysioNet (http://www.physionet.org/) is a web-based resource supplying well-characterized physiologic signals and related open-source software to the biomedical research community. Inaugurated in September 1999 under the auspices of the NIH's National Center for Research Resources (NCRR), PhysioNet provides an on-line forum for free dissemination and exchange of research data and software, with facilities for cooperative analysis of data and evaluation of new analytic methods. As of September 2000, PhysioBank, the data archive made available via PhysioNet, contained roughly 35 gigabytes of recorded signals and annotations. PhysioNet is a public service of the Research Resource for Complex Physiologic Signals, a cooperative project initiated by researchers at Boston's Beth Israel Deaconess Medical Center/Harvard Medical School, Boston University, McGill University, and MIT.