Integrated physiological systems, such as the cardiac and the respiratory system, exhibit complex dynamics that are further influenced by intrinsic feedback mechanisms controlling their interaction. To probe how the cardiac and the respiratory system adjust their rhythms, despite continuous fluctuations in their dynamics, we study the phase synchronization of heartbeat intervals and respiratory cycles. The nature of this interaction, its physiological and clinical relevance, and its relation to mechanisms of neural control is not well understood. We investigate whether and how cardiorespiratory phase synchronization (CRPS) responds to changes in physiological states and conditions. We find that the degree of CRPS in healthy subjects dramatically changes with sleep-stage transitions and exhibits a pronounced stratification pattern with a 400% increase from rapid eye movement sleep and wake, to light and deep sleep, indicating that sympatho-vagal balance strongly influences CRPS. For elderly subjects, we find that the overall degree of CRPS is reduced by approximately 40%, which has important clinical implications. However, the sleep-stage stratification pattern we uncover in CRPS does not break down with advanced age, and surprisingly, remains stable across subjects. Our results show that the difference in CRPS between sleep stages exceeds the difference between young and elderly, suggesting that sleep regulation has a significantly stronger effect on cardiorespiratory coupling than healthy aging. We demonstrate that CRPS and the traditionally studied respiratory sinus arrhythmia represent different aspects of the cardiorespiratory interaction, and that key physiologic variables, related to regulatory mechanisms of the cardiac and respiratory systems, which influence respiratory sinus arrhythmia, do not affect CRPS.

[1] Applying a simple general procedure for identifying aftershocks, we investigate their statistical properties for a high-resolution earthquake catalog covering Southern California. We compare our results with those obtained by using other methods in order to show which features truly characterize aftershock sequences and which depend on the definition of aftershocks. Features robust across methods include the p value in the Omori-Utsu law for large main shocks, Båth's law, and the productivity law with an exponent smaller than the b value in the Gutenberg-Richter law. The identification of a typical aftershock distance with the rupture length is a feature we confirm as well as a power law decay in the spatial distribution of aftershocks with an exponent less than 2. Other results we obtain, but not common to all other works including Marsan and Lengliné (2008), Zhuang et al. (2008), are (a) p values that do not increase with the main shock magnitude; (b) the duration of bare aftershock sequences that scales with the main shock magnitude; (c) an additional power law in the temporal variation, at intermediate times, in the rate of aftershocks for main shocks of small and intermediate magnitude; and (d) a b value for the Gutenberg-Richter law of background events that is sensibly larger than that of aftershocks. Tests on synthetic catalogs generated by the epidemic-type aftershock sequence model corroborate the validity of our approach.

The age dependence of alpha1 should be considered when using this value for diagnostic purposes in post-infarction patients. Pronounced long-term correlations (larger alpha2) for heartbeat and respiration during REM sleep and wake indicate an enhanced control of higher brain regions, which is absent during NREM sleep. Reduced DC possibly indicates an increased cardiovascular risk with aging and during REM and deep sleep.

We present the phase-rectified signal averaging (PRSA) method as an efficient technique for the study of quasi-periodic oscillations in noisy, nonstationary signals. It allows the assessment of system dynamics despite phase resetting and noise and in relation with either increases or decreases of the considered signal. We employ the method to study the quasi-periodicities of the human heart rate based on long-term ECG recordings. The center deflection of the PRSA curve characterizes the average capacity of the heart to decelerate (or accelerate) the cardiac rhythm. It can be measured by a central wavelet coefficient which we denote as deceleration capacity (DC). We find that decreased DC is a more precise predictor of mortality in survivors of heart attack than left ventricular ejection fraction, the current "gold standard" risk predictor. In addition, we discuss the dependence of the DC parameter on age and on diabetes.

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The dynamics of complex systems is characterized by oscillatory components on many time scales. To study the interactions between these components we analyze the cross modulation of their instantaneous amplitudes and frequencies, separating synchronous and antisynchronous modulation. We apply our novel technique to brain-wave oscillations in the human electroencephalogram and show that interactions between the alpha wave and the delta or beta wave oscillators as well as spatial interactions can be quantified and related with physiological conditions (e.g., sleep stages). Our approach overcomes the limitation to oscillations with similar frequencies and enables us to quantify directly nonlinear effects such as positive or negative frequency modulation.

We study short-term and long-term persistence properties (related with autocorrelations) of amplitudes and frequencies of EEG oscillations in 176 healthy subjects and 40 patients during nocturnal sleep. The amplitudes show scaling from 2 to 500 seconds (depending on the considered band) with large fluctuation exponents during (nocturnal) wakefulness (0.73-0.83) and small ones during deep sleep (0.50-0.69). Light sleep is similar to deep sleep, while REM sleep (0.64-0.76) is closer to wakefulness except for the EEG γ band. Some of the frequency time series also show long-term scaling, depending on the selected bands and stages. Only minor deviations are seen for patients with depression, anxiety, or Parkinson's disease.