Biomedical subjects

Jan W Kantelhardt

Publications and source records attributed to Jan W Kantelhardt.

8 recordsLinked to original sources

PubMed2006

Detrended fluctuation analysis in x-ray photon correlation spectroscopy for determining coarsening dynamics in alloys.

We study the dynamics of precipitate coarsening in phase-separating alloys at late stages of phase separation by x-ray photon correlation spectroscopy (XPCS). For analyzing time series of fluctuating speckle intensities from small-angle scattering of coherent x rays, the method of detrended fluctuation analysis (DFA), which is ideal for determining power-law correlations, is applied. We discuss the application of DFA with respect to XPCS data by means of simulated time series. In particular, the effects of different signal-to-noise ratios are examined. Results from measurements of the two model systems Al-6 at. % Ag at 140 degrees C and Al-9 at. % Zn at 0 degrees C are presented. Since the DFA effectively removes adulterating trends in the data, quantitative agreement with Monte Carlo simulations is obtained. It is verified that two different coarsening mechanisms are predominant in the two systems--coarsening either by diffusion of single atoms or by movement of whole precipitates.

Journal Article↗

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

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↗

PubMed2005

Nonrandom variability of respiration during sleep in healthy humans.

STUDY OBJECTIVES: Breath-to-breath variability is not purely random but is, instead, characterized by correlations on short- and long-term scales. Short-term correlations might reflect intact metabolic-control mechanisms. To investigate whether the higher variability of breathing during rapid eye movement (REM) compared to non-REM (NREM) sleep is of random or nonrandom nature--reflecting an altered respiratory control--short-term and long-term correlations of respiratory drive and timing were determined. DESIGN: A full-night polysomnogram with a pneumotachograph attached to a full-face mask was performed. For each breath during NREM and REM sleep, respiratory components were analyzed based on the quantitative airflow. SETTING: Data collection took place in the sleep laboratory. PARTICIPANTS: Twenty-nine healthy subjects (age, 25.8 +/- 3.1 years). MEASUREMENTS AND RESULTS: Long-term correlations are practically absent in respiratory timing and drive components during NREM sleep, whereas they are present during REM sleep. Short-term correlations are present in respiratory drive, tidal volume, and minute ventilation during both NREM and REM sleep. In all timing components, additional short-term correlations are absent. CONCLUSION: We conclude that from NREM to REM sleep, short-term regulation of respiratory drive remains strongly metabolically controlled and clearly different from the short-term regulation of the rhythm-generating function. Regulation of respiratory timing and drive during REM sleep is characterized by additional long-term correlations. We speculate that this is the result of cortical influences during phasic REM sleep. Thus, the variability of breathing during REM sleep contains a nonrandom component, such that breathing components remain dependent upon each other even with large time lags between components.

Adult↗

PubMed2003

Dynamics of heart rate and sleep stages in normals and patients with sleep apnea.

Sleep is not just the absence of wakefulness but a regulated process with an important restorative function. Based on electroencephalographic recordings and characteristic patterns and waveforms we can distinguish wakefulness and five sleep stages grouped into light sleep, deep sleep, and rapid-eye-movement (REM) sleep. In order to explore the functions of sleep and sleep stages, we investigated the dynamics of sleep stages over the night and of heart-rate variability during the different sleep stages. Sleep stages and intermediate wake states have different distributions of their duration and this allows us to create a model for the temporal sequence of sleep stages and wake states. Heart rate is easily accessed with a high precision by the recording and analysis of the electrocardiogram (ECG). Heart-rate regulation is part of the autonomous nervous system and sympathetic tone is strongly influenced by the sleep stages.

Heart Rate↗

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

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↗