Madalena D. Costa

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.

Multiscale Analysis of Heart Rate Dynamics: Entropy and Time Irreversibility Measures

Cardiovascular signals are largely analyzed using traditional time and frequency domain measures. However, such measures fail to account for important properties related to multiscale organization and non-equilibrium dynamics. The complementary role of conventional signal analysis methods and emerging multiscale techniques, is, therefore, an important frontier area of investigation. The key finding of this presentation is that two recently developed multiscale computational tools––multiscale entropy and multiscale time irreversibility––are able to extract information from cardiac interbeat interval time series not contained in traditional methods based on mean, variance or Fourier spectrum (two-point correlation) techniques. These new methods, with careful attention to their limitations, may be useful in diagnostics, risk stratification and detection of toxicity of cardiac drugs.

Physiological complexity and system adaptability: evidence from postural control dynamics of older adults

The degree of multiscale complexity in human behavioral regulation, such as that required for postural control, appears to decrease with advanced aging or disease. To help delineate causes and functional consequences of complexity loss, we examined the effects of visual and somatosensory impairment on the complexity of postural sway during quiet standing and its relationship to postural adaptation to cognitive dual tasking. Participants of the MOBILIZE Boston Study were classified into mutually exclusive groups: controls [intact vision and foot somatosensation, n = 299, 76 ± 5 (SD) yr old], visual impairment only (<20/40 vision, n = 81, 77 ± 4 yr old), somatosensory impairment only (inability to perceive 5.07 monofilament on plantar halluxes, n = 48, 80 ± 5 yr old), and combined impairments (n = 25, 80 ± 4 yr old). Postural sway (i.e., center-of-pressure) dynamics were assessed during quiet standing and cognitive dual tasking, and a complexity index was quantified using multiscale entropy analysis. Postural sway speed and area, which did not correlate with complexity, were also computed. During quiet standing, the complexity index (mean ± SD) was highest in controls (9.5 ± 1.2) and successively lower in the visual (9.1 ± 1.1), somatosensory (8.6 ± 1.6), and combined (7.8 ± 1.3) impairment groups (P = 0.001). Dual tasking resulted in increased sway speed and area but reduced complexity (P < 0.01). Lower complexity during quiet standing correlated with greater absolute (R = -0.34, P = 0.002) and percent (R = -0.45, P < 0.001) increases in postural sway speed from quiet standing to dual-tasking conditions. Sensory impairments contributed to decreased postural sway complexity, which reflected reduced adaptive capacity of the postural control system. Relatively low baseline complexity may, therefore, indicate control systems that are more vulnerable to cognitive and other stressors.

Frailty and the Degradation of Complex Balance Dynamics During a Dual-Task Protocol

BACKGROUND Balance during quiet stance involves the complex interactions of multiple postural control systems, which may degrade with frailty. The complexity of center of pressure (COP) dynamics, as quantified using multiscale entropy (MSE), during quiet standing is lower in older adults, especially those with falls. We hypothesized that COP dynamics from frail elderly individuals demonstrate less complexity than those from nonfrail elderly controls; complexity decreases when performing a dual task; and postural complexity during quiet standing is independent of other conventional correlates of balance control, such as age and vision. METHODS We analyzed data from a population-based study of community-dwelling older adults. Frailty phenotype (nonfrail, prefrail, or frail) was determined for 550 participants (age 77.9 +/- 5.5 years). COP excursions were quantified for 10 trials of 30 seconds each. Participants concurrently performed a serial subtraction task in half of the trials. Complexity of balance dynamics was quantified using MSE. Root-mean-square sway amplitude was also computed. RESULTS Of the 550, 38% were prefrail and 9% were frail. Complexity of the COP dynamics in the anteroposterior direction was lower in prefrail (8.78 +/- 1.91 [mean +/- SD]) and frail (8.38 +/- 2.13) versus nonfrail (9.20 +/- 1.74) groups (p < .001). Complexity reduced by a comparable amount in all three groups while performing the subtraction task (p < .001). Quiet standing complexity was independently associated with frailty after adjusting for covariates related to balance while sway amplitude was not. CONCLUSION Cognitive distractions during standing may further compromise balance control in frail individuals, leading to an increased risk of falls.

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.

ADAPTIVE DATA ANALYSIS OF COMPLEX FLUCTUATIONS IN PHYSIOLOGIC TIME SERIES

We introduce a generic framework of dynamical complexity to understand and quantify fluctuations of physiologic time series. In particular, we discuss the importance of applying adaptive data analysis techniques, such as the empirical mode decomposition algorithm, to address the challenges of nonlinearity and nonstationarity that are typically exhibited in biological fluctuations.

Multiscale entropy analysis of complex heart rate dynamics: discrimination of age and heart failure effects

Quantifying the complexity of physiologic time series has been of considerable interest. Several entropy-based measures have been proposed, although there is no straightforward correspondence between entropy and complexity. These traditional algorithms may generate misleading results because an increase in system entropy is not always associated with an increase in its complexity, and because the algorithms are based on single time scales. Recently, we introduced a new method, multiscale entropy (MSE) analysis, to calculate entropy over a wide range of scales. In this study, we sought to determine whether loss of complexity due to aging could be distinguished from that due to major cardiac pathology. We analyzed RR time series from young subjects (n = 26), elderly subjects (n = 46) and subjects with congestive heart failure (n = 43). The mean MSE measures of each of the three groups revealed characteristic curves, suggesting that they capture fundamental changes in the heart rate dynamics due to age and disease. We used Fishers linear discriminant to evaluate the use of MSE features for classification. In discriminant tests on the training data, we found that MSE features could separate elderly, young and heart failure subjects with 92% accuracy and that older healthy subjects (mean age = 65.9) could be separated from subjects with heart failure (mean age = 55.5) with 94% accuracy. Also, we discriminated data from heart failure subjects and elderly healthy subjects with a positive predictivity of 76% and a specificity of 83% using only the MSE features. Larger databases will be needed to confirm if automatic classification results can match separation results. We conclude that MSE features capture differences in complexity due to aging and heart failure. These differences have implications for modeling neuroautonomic perturbations in health and disease.

Generalized Multiscale Entropy Analysis: Application to Quantifying the Complex Volatility of Human Heartbeat Time Series

We introduce a generalization of multiscale entropy (MSE) analysis. The method is termed MSEn, where the subscript denotes the moment used to coarse-grain a time series. MSEμ, described previously, uses the mean value (first moment). Here, we focus on MSEσ2, which uses the second moment, i.e., the variance. MSEσ2 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.

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.

Complexity-Based Measures Inform Effects of Tai Chi Training on Standing Postural Control: Cross-Sectional and Randomized Trial Studies

Background Diminished control of standing balance, traditionally indicated by greater postural sway magnitude and speed, is associated with falls in older adults. Tai Chi (TC) is a multisystem intervention that reduces fall risk, yet its impact on sway measures vary considerably. We hypothesized that TC improves the integrated function of multiple control systems influencing balance, quantifiable by the multi-scale “complexity” of postural sway fluctuations. Objectives To evaluate both traditional and complexity-based measures of sway to characterize the short- and potential long-term effects of TC training on postural control and the relationships between sway measures and physical function in healthy older adults. Methods A cross-sectional comparison of standing postural sway in healthy TC-naïve and TC-expert (24.5±12 yrs experience) adults. TC-naïve participants then completed a 6-month, two-arm, wait-list randomized clinical trial of TC training. Postural sway was assessed before and after the training during standing on a force-plate with eyes-open (EO) and eyes-closed (EC). Anterior-posterior (AP) and medio-lateral (ML) sway speed, magnitude, and complexity (quantified by multiscale entropy) were calculated. Single-legged standing time and Timed-Up–and-Go tests characterized physical function. Results At baseline, compared to TC-naïve adults (n = 60, age 64.5±7.5 yrs), TC-experts (n = 27, age 62.8±7.5 yrs) exhibited greater complexity of sway in the AP EC (P = 0.023), ML EO (P<0.001), and ML EC (P<0.001) conditions. Traditional measures of sway speed and magnitude were not significantly lower among TC-experts. Intention-to-treat analyses indicated no significant effects of short-term TC training; however, increases in AP EC and ML EC complexity amongst those randomized to TC were positively correlated with practice hours (P = 0.044, P = 0.018). Long- and short-term TC training were positively associated with physical function. Conclusion Multiscale entropy offers a complementary approach to traditional COP measures for characterizing sway during quiet standing, and may be more sensitive to the effects of TC in healthy adults. Trial Registration ClinicalTrials.gov NCT01340365