PubMed Health⌕ Search

Biomedical subjects

C-K Peng

Publications and source records attributed to C-K Peng.

11 recordsLinked to original sources
PubMed2005

Broken asymmetry of the human heartbeat: loss of time irreversibility in aging and disease.

Time irreversibility, a fundamental property of nonequilibrium systems, should be of importance in assessing the status of physiological processes that operate over a wide range of scales. However, measurement of this property in living systems has been limited. We provide a computational method derived from basic physics assumptions to quantify time asymmetry over multiple scales and apply it to the human heartbeat time series in health and disease. We find that the multiscale time asymmetry index is highest for a time series from young subjects and decreases with aging or heart disease. Loss of time irreversibility may provide a new way of assessing the functionality of living systems that operate far from equilibrium.

Aging↗
PubMed2005

Multiscale entropy analysis of biological signals.

Traditional approaches to measuring the complexity of biological signals fail to account for the multiple time scales inherent in such time series. These algorithms have yielded contradictory findings when applied to real-world datasets obtained in health and disease states. We describe in detail the basis and implementation of the multiscale entropy (MSE) method. We extend and elaborate previous findings showing its applicability to the fluctuations of the human heartbeat under physiologic and pathologic conditions. The method consistently indicates a loss of complexity with aging, with an erratic cardiac arrhythmia (atrial fibrillation), and with a life-threatening syndrome (congestive heart failure). Further, these different conditions have distinct MSE curve profiles, suggesting diagnostic uses. The results support a general "complexity-loss" theory of aging and disease. We also apply the method to the analysis of coding and noncoding DNA sequences and find that the latter have higher multiscale entropy, consistent with the emerging view that so-called "junk DNA" sequences contain important biological information.

Adult↗
PubMed2005

Genomic classification using an information-based similarity index: application to the SARS coronavirus.

Measures of genetic distance based on alignment methods are confined to studying sequences that are conserved and identifiable in all organisms under study. A number of alignment-free techniques based on either statistical linguistics or information theory have been developed to overcome the limitations of alignment methods. We present a novel alignment-free approach to measuring the similarity among genetic sequences that incorporates elements from both word rank order-frequency statistics and information theory. We first validate this method on the human influenza A viral genomes as well as on the human mitochondrial DNA database. We then apply the method to study the origin of the SARS coronavirus. We find that the majority of the SARS genome is most closely related to group 1 coronaviruses, with smaller regions of matches to sequences from groups 2 and 3. The information based similarity index provides a new tool to measure the similarity between datasets based on their information content and may have a wide range of applications in the large-scale analysis of genomic databases.

Base Sequence↗
PubMed2004

Heart rate dynamics during three forms of meditation.

OBJECTIVE: This study was designed to quantify and compare the instantaneous heart rate dynamics and cardiopulmonary interactions during sequential performance of three meditation protocols with different breathing patterns. BACKGROUND: We analyzed beat-to-beat heart rate and continuous breathing signals from 10 experienced meditators (4 females; 6 males; mean age 42 years; range 29-55 years) during three traditional interventions: relaxation response, breath of fire, and segmented breathing. RESULTS: Heart rate and respiratory dynamics were generally similar during the relaxation response and segmented breathing. We observed high amplitude, low frequency (approximately 0.05-0.1 Hz) oscillations due to respiratory sinus arrhythmia during both the relaxation response and segmented breathing, along with a significantly (p<0.05) increased coherence between heart rate and breathing during these two maneuvers when compared to baseline. The third technique, breath of fire, was associated with a different pattern of response, marked by a significant increase in mean heart rate with respect to baseline (p<0.01), and a significant decrease in coherence between heart rate and breathing (p<0.05). CONCLUSIONS: These findings suggest that different meditative/breathing protocols may evoke common heart rate effects, as well as specific responses. The results support the concept of a "meditation paradox," since a variety of relaxation and meditative techniques may produce active rather than quiescent cardiac dynamics, associated with prominent low frequency heart rate oscillations or increases in mean resting heart rate. These findings also underscore the need to critically assess traditional frequency domain heart rate variability parameters in making inferences about autonomic alterations during meditation with slow breathing.

Adult↗
PubMed2003

Linguistic analysis of the human heartbeat using frequency and rank order statistics.

Complex physiologic signals may carry unique dynamical signatures that are related to their underlying mechanisms. We present a method based on rank order statistics of symbolic sequences to investigate the profile of different types of physiologic dynamics. We apply this method to heart rate fluctuations, the output of a central physiologic control system. The method robustly discriminates patterns generated from healthy and pathologic states, as well as aging. Furthermore, we observe increased randomness in the heartbeat time series with physiologic aging and pathologic states and also uncover nonrandom patterns in the ventricular response to atrial fibrillation.

Adult↗
PubMed2003

Diurnal and ultradian dynamics of serum adiponectin in healthy men: comparison with leptin, circulating soluble leptin receptor, and cortisol patterns.

Adiponectin is an abundant serum adipokine secreted exclusively from differentiated adipocytes, which plays an important role in regulating insulin sensitivity. The dynamics of circulating adiponectin concentrations have yet to be systematically investigated. We sought to determine whether serum adiponectin levels exhibit diurnal or ultradian rhythms in healthy normal-weight men and to compare the 24-h profile of adiponectin fluctuations with those of leptin, leptin-binding protein (sOB-R), and cortisol. We collected blood samples at 15-min intervals over 24 h from six subjects receiving an isocaloric diet, and we measured adiponectin, leptin, sOB-R, and cortisol levels. Fourier and cross-correlation analyses were performed on these time series to study diurnal variations, and the Cluster7 program was used for pulsatility analysis. Circulating adiponectin and sOB-R levels exhibited ultradian pulsatility as well as a diurnal variation with a significant decline at night, reaching a nadir in the early morning. The 24-h variations of serum adiponectin and sOB-R were nearly identical and followed those of cortisol after a few hours, but were out-of-phase with leptin diurnal rhythms. These data suggest that adiponectin and sOB-R levels might be influenced by common regulatory factors and challenge the notion that cortisol may have a direct inhibitory effect on adiponectin in humans.

Activity Cycles↗
PubMed2002

Multiscale entropy analysis of complex physiologic time series.

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.

Algorithms↗
PubMed2002

Fractal dynamics in physiology: alterations with disease and aging.

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.

Aging↗
PubMed2002

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.

Adult↗
PubMed2002

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.

Adult↗