Biomedical subjects

Armin Bunde

Publications and source records attributed to Armin Bunde.

12 recordsLinked to original sources

PubMed2006

Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study.

BACKGROUND: Decreased vagal activity after myocardial infarction results in reduced heart-rate variability and increased risk of death. To distinguish between vagal and sympathetic factors that affect heart-rate variability, we used a signal-processing algorithm to separately characterise deceleration and acceleration of heart rate. We postulated that diminished deceleration-related modulation of heart rate is an important prognostic marker. Our prospective hypotheses were that deceleration capacity is a better predictor of risk than left-ventricular ejection fraction (LVEF) and standard deviation of normal-to-normal intervals (SDNN). METHODS: We quantified heart rate deceleration capacity by assessing 24-h Holter recordings from a post-infarction cohort in Munich (n=1455). We blindly validated the prognostic power of deceleration capacity in post-infarction populations in London, UK (n=656), and Oulu, Finland (n=600). We tested our hypotheses by assessment of the area under the receiver-operator characteristics curve (AUC). FINDINGS: During a median follow-up of 24 months, 70 people died in the Munich cohort and 66 in the London cohort. The Oulu cohort was followed-up for 38 months and 77 people died. In the London cohort, mean AUC of deceleration capacity was 0.80 (SD 0.03) compared with 0.67 (0.04) for LVEF and 0.69 (0.04) for SDNN. In the Oulu cohort, mean AUC of deceleration capacity was 0.74 (0.03) compared with 0.60 (0.04) for LVEF and 0.64 (0.03) for SDNN (p<0.0001 for all comparisons). Stratification by dichotomised deceleration capacity was especially powerful in patients with preserved LVEF (p<0.0001 in all cohorts). INTERPRETATION: Impaired heart rate deceleration capacity is a powerful predictor of mortality after myocardial infarction and is more accurate than LVEF and the conventional measures of heart-rate variability.

Aged↗

PubMed2006

Extreme value statistics in records with long-term persistence.

Many natural records exhibit long-term correlations characterized by a power-law decay of the autocorrelation function, C(s) approximately s-gamma, with time lag s and correlation exponent 0<gamma<1. We study how the presence of such correlations affects the statistics of the extreme events, i.e., the maximum values of the signal within time segments of the fixed duration R. We find numerically that (i) the integrated distribution function of the maxima converges to a Gumbel distribution for large R similar to uncorrelated signals, (ii) the deviations for finite R depend on the initial distribution of the records and on their correlation properties, (iii) the maxima series exhibit long-term correlations similar to those of the original data, and most notably (iv) the maxima distribution as well as the mean maxima significantly depend on the history, in particular on the previous maximum. The last item implies that conditional mean maxima and conditional maxima distributions (with the value of the previous maximum as condition) should be considered for an improved extreme event prediction. We provide indications that this dependence of the mean maxima on the previous maximum occurs also in observational long-term correlated records.

Journal Article↗

PubMed2005

Memory in the occurrence of earthquakes.

We study the statistics of the recurrence times tau between earthquakes above a certain magnitude M in six (one global and five regional) earthquake catalogs. We find that the distribution of the recurrence times strongly depends on the previous recurrence time tau0, such that small and large recurrence times tend to cluster in time. This dependence on the past is reflected in both the conditional mean recurrence time and the conditional mean residual time until the next earthquake, which increase monotonically with tau0. As a consequence, the risk of encountering the next event within a certain time span after the last event depends significantly on the past, an effect that has to be taken into account in any effective earthquake prognosis.

Journal Article↗

PubMed2005

Lambert diffusion in porous media in the Knudsen regime: equivalence of self-diffusion and transport diffusion.

We study molecular diffusion in nanopores with different types of roughness under the exclusion of mutual molecular collisions, i.e., in the so-called Knudsen regime. We show that the diffusion problem can be mapped onto Levy walks and discuss the roughness dependence of the diffusion coefficients D(s) and D(t) of self- and transport diffusion, respectively. While diffusion is normal in d=3, diffusion is anomalous in d=2 with D(s) approximately ln t and D(t) approximately ln L, where t and L are time and system size, respectively. Both diffusion coefficients decrease significantly when the roughness is enhanced, in remarkable disagreement with earlier findings.

Journal Article↗

PubMed2005

Scaling and memory in volatility return intervals in financial markets.

For both stock and currency markets, we study the return intervals tau between the daily volatilities of the price changes that are above a certain threshold q. We find that the distribution function Pq(tau) scales with the mean return interval tau as Pq(tau)=tau(-1)f(tau/tau). The scaling function fx is similar in form for all seven stocks and for all seven currency databases analyzed, and fx is consistent with a power-law form, fx approximately x(-gamma) with gamma approximately 2. We also quantify how the conditional distribution Pq(tau/tau0) depends on the previous return interval tau0 and find that small (or large) return intervals are more likely to be followed by small (or large) return intervals. This "clustering" of the volatility return intervals is a previously unrecognized phenomenon that we relate to the long-term correlations known to be present in the volatility.

Journal Article↗

PubMed2005

Stability and topology of scale-free networks under attack and defense strategies.

We study tolerance and topology of random scale-free networks under attack and defense strategies that depend on the degree k of the nodes. This situation occurs, for example, when the robustness of a node depends on its degree or in an intentional attack with insufficient knowledge of the network. We determine, for all strategies, the critical fraction p(c) of nodes that must be removed for disintegrating the network. We find that, for an intentional attack, little knowledge of the well-connected sites is sufficient to strongly reduce p(c). At criticality, the topology of the network depends on the removal strategy, implying that different strategies may lead to different kinds of percolation transitions.

Animals↗

PubMed2005

Long-term memory: a natural mechanism for the clustering of extreme events and anomalous residual times in climate records.

We study the statistics of the return intervals between extreme events above a certain threshold in long-term persistent records. We find that the long-term memory leads (i) to a stretched exponential distribution of the return intervals, (ii) to a pronounced clustering of extreme events, and (iii) to an anomalous behavior of the mean residual time to the next event that depends on the history and increases with the elapsed time in a counterintuitive way. We present an analytical scaling approach and demonstrate that all these features can be seen in long climate records. The phenomena should also occur in heartbeat records, Internet traffic, and stock market volatility and have to be taken into account for an efficient risk evaluation.

Algorithms↗

PubMed2004

Diffusion-driven spreading phenomena: the structure of the hull of the visited territory.

We study the hull of the territory visited by N random walkers after t time steps. The walkers move on two-dimensional substrates, starting all from the same position. For the substrate, we consider (a). a square lattice and (b). a percolation cluster at criticality. On the square lattice, we (c). also allow for birth and death processes, where at every time step, alphaN walkers die and are removed from the substrate, and simultaneously the same number of walkers is added randomly at the positions of the remaining walkers, such that the total numbers of walkers is constant in time. We perform numerical simulations for the three processes and find that for all of them, the structure of the hull is self-similar and described by a fractal dimension d(H) that slowly approaches, with an increasing number of time steps, the value d(H)=4/3. For process (c), however, the time to approach the asymptotic value increases drastically with increasing fraction of N/alpha, and can be observed numerically only for sufficiently small values of N/alpha.

Journal Article↗

PubMed2003

Nonlinear volatility of river flux fluctuations.

We study the spectral properties of the magnitudes of daily river flux increments, the volatility. The volatility series exhibits (i) strong seasonal periodicity and (ii) power-law correlations for time scales less than 1 yr. We test the nonlinear properties of the river flux increment series by randomizing its Fourier phases and find that the surrogate volatility series (i) has almost no seasonal periodicity and (ii) is weakly correlated for time scales less than 1 yr. We quantify the degree of nonlinearity by measuring (i) the amplitude of the power spectrum at the seasonal peak and (ii) the correlation power-law exponent of the volatility series.

Journal Article↗

PubMed2003

Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea.

Sleep has been regarded as a testing situation for the autonomic nervous system, because its activity is modulated by sleep stages. Sleep-related breathing disorders also influence the autonomic nervous system and can cause heart rate changes known as cyclical variation. We investigated the effect of sleep stages and sleep apnea on autonomic activity by analyzing heart rate variability (HRV). Since spectral analysis is suited for the identification of cyclical variations and detrended fluctuation analysis can analyze the scaling behavior and detect long-range correlations, we compared the results of both complementary techniques in 14 healthy subjects, 33 patients with moderate, and 31 patients with severe sleep apnea. The spectral parameters VLF, LF, HF, and LF/HF confirmed increasing parasympathetic activity from wakefulness and REM over light sleep to deep sleep, which is reduced in patients with sleep apnea. Discriminance analysis was used on a person and sleep stage basis to determine the best method for the separation of sleep stages and sleep apnea severity. Using spectral parameters 69.7% of the apnea severity assignments and 54.6% of the sleep stage assignments were correct, while using scaling analysis these numbers increased to 74.4% and 85.0%, respectively. We conclude that changes in HRV are better quantified by scaling analysis than by spectral analysis.

Adult↗

PubMed2002

Global climate models violate scaling of the observed atmospheric variability.

We test the scaling performance of seven leading global climate models by using detrended fluctuation analysis. We analyze temperature records of six representative sites around the globe simulated by the models, for two different scenarios: (i) with greenhouse gas forcing only and (ii) with greenhouse gas plus aerosol forcing. We find that the simulated records for both scenarios fail to reproduce the universal scaling behavior of the observed records and display wide performance differences. The deviations from the scaling behavior are more pronounced in the first scenario, where also the trends are clearly overestimated.

Journal Article↗

PubMed2002

Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments.

We study correlation properties of the magnitude and the sign of the increments in the time intervals between successive heartbeats during light sleep, deep sleep, and rapid eye movement (REM) sleep using the detrended fluctuation analysis method. We find short-range anticorrelations in the sign time series, which are strong during deep sleep, weaker during light sleep, and even weaker during REM sleep. In contrast, we find long-range positive correlations in the magnitude time series, which are strong during REM sleep and weaker during light sleep. We observe uncorrelated behavior for the magnitude during deep sleep. Since the magnitude series relates to the nonlinear properties of the original time series, while the sign series relates to the linear properties, our findings suggest that the nonlinear properties of the heartbeat dynamics are more pronounced during REM sleep. Thus, the sign and the magnitude series provide information which is useful in distinguishing between the sleep stages.

Biophysics↗