Transient phase locking patterns among respiration, heart rate and blood pressure during cardiorespiratory synchronisation in humans

Rhythmic firing of neurons in the medulla of conscious freely behaving rats: rhythmic coupling with baroreceptor input

Recent investigations emphasized the importance of neural control of cardiovascular adjustments in complex behaviors, including stress, exercise, arousal, sleep-wake states, and different tasks. Baroreceptor feedback is an essential component of this system acting on different time scales from maintaining stable levels of cardiovascular parameters on the long-term to rapid alterations according to behavior. The baroreceptor input is essentially rhythmic, reflecting periodic fluctuations in arterial blood pressure. Cardiac rhythm is a prominent feature of the autonomic control system, present on different levels, including neuron activity in central circuits. The mechanism of rhythmic entrainment of neuron firing by the baroreceptor input was studied in great detail under anesthesia, but recordings of sympathetic-related neuron firing in freely moving animals remain extremely scarce. In this study, we recorded multiple single neuron activity in the reticular formation of the medulla in freely moving rats during natural behavior. Neurons firing in synchrony with the cardiac rhythm were detected in each experiment (n = 4). In agreement with prior observations in anesthetized cats, we found that neurons in this area exhibited high neuron-to-neuron variability and temporal flexibility in their coupling to cardiac rhythm in freely moving rats, as well. This included firing in bursts at multiples of cardiac cycles, but not directly coupled to the heartbeat, supporting the concept of baroreceptor input entraining intrinsic neural oscillations rather than imposing a rhythm of solely external origin on these networks. It may also point to a mechanism of maintaining the basic characteristics of sympathetic neuron activity, i.e., burst discharge and cardiac-related rhythmicity, on the background of behavior-related adjustments in their firing rate.

The effect of ketamine on delta-range coupling between prefrontal cortex and hippocampus supported by respiratory rhythmic input from the olfactory bulb

Respiratory rhythm plays an important role in cognitive functions in rodents, as well as in humans. Respiratory related oscillation (RRO), generated in the olfactory bulb (OB), is an extrinsic rhythm imposed on brain networks. In rats, RRO can couple with intrinsic brain oscillations at theta frequency during sniffing and in the delta range outside of such episodes. Disruption of gamma synchronization in cortical networks by ketamine is well established whereas its effects on slow rhythms are poorly understood. We found in this study, that RRO in prefrontal cortex (PFC) and hippocampus (HC) remains present after ketamine injection, even on the background of highly unstable respiratory rate, co-incident with "psychotic-like" behavior and abnormal cortical gamma activity. Guided by the timing of ketamine-induced gamma reaction, pairwise coherences between structures exhibiting RRO and their correlation structure was statistically tested in 5-min segments post-injection (0-25 min) and during recovery (1, 5, 10 h). As in control, RRO in the OB was firmly followed by cortical-bound OB exits directed toward PFC but not to HC. RRO between these structures, however, significantly correlated with OB-HC but not with OB-PFC. The only exception to this general observation was observed during a short transitional period, immediately after injection. Ketamine has a remarkable history in psychiatric research. Modeling chronic NMDA-hypofunction using acute NMDA-receptor blockade shifted the primary focus of schizophrenia research to dysfunctional cortical microcircuitry and the recent discovery of ketamine's antidepressant actions extended investigations to neurophysiology of anxiety and depression. Cortical oscillations are relevant for understanding their pathomechanism.

Association of Cardiac Autonomic Responses with Clinical Outcomes of Myasthenia Gravis: Short-Term Analysis of the Heart-Rate and Blood Pressure Variability

Introduction: The aim of the study was to assess cardiac and autonomic function in patients with myasthenia gravis (MG) and to explore its relationship with disease outcomes. Methods: Thirty-eight patients with an MG were enrolled (median age 40.5 years; median disease duration 5.5 years). Cardiovascular parameters, baroreflex sensitivity (BRS), spectral indices of short-term heart rate (HRV), and systolic blood pressure variability (SBPV) were compared with age- and gender-matched controls (n = 30). Cardiac autonomic function was assessed during the response to standing (tilt) and deep breathing tests (expiration/inspiration ratio-E/I). Results: HR and BP responses to the tilt test were similar in both groups. MG patients, as compared to controls, were characterized by altered SBPV at rest, significantly reduced HR response to the deep breathing test (p < 0.001), increased sympathovagal balance after tilt (delta LF/HF-RRI, p = 0.037), and lower values of BRS (p = 0.007) and hemodynamic parameters, i.e., cardiac index, index contractility, left ventricular work index, at rest and during tilt. There was no association between disease duration and autonomic parameters. Disease severity, as determined by MGFA (Myasthenia Gravis Foundation of America) corrected for age and sex, was an independent predictor of diminished vagal tone (E/I ratio) and increased sympathetic response to tilt (delta LF/HF-RRI) as measured with HRV. Lower BRS was associated with greater disease severity and older age. Hemodynamic parameters were predominantly predicted by age and sex. Conclusion: Our results confirm cardiac autonomic dysfunction among MG patients with predominant parasympathetic impairment. Clinicians should consider evaluation of autonomic balance in MG patients with, or at risk for, cardiovascular disease.

Blood pressure drives multispectral tuning of inspiration via a linked-loop neural network

The respiratory motor pattern is coordinated with cardiovascular system regulation. Inspiratory drive and respiratory phase durations are tuned by blood pressure and baroreceptor reflexes. We hypothesized that perturbations of systemic arterial blood pressure modulate inspiratory drive through a raphe-pontomedullary network. In 15 adult decerebrate vagotomized neuromuscular-blocked cats, we used multielectrode arrays to record the activities of 704 neurons within the medullary ventral respiratory column, pons, and raphe areas during baroreceptor-evoked perturbations of breathing, as measured by altered peak activity in integrated efferent phrenic nerve activity and changes in respiratory phase durations. Blood pressure was transiently (30 s) elevated or reduced by inflations of an embolectomy catheter in the descending aorta or inferior vena cava. S-transform time-frequency representations were calculated for multiunit phrenic nerve activity and some spike trains to identify changes in rhythmic activity during perturbations. Altered firing rates in response to either or both conditions were detected for 474 of 704 tested cells. Spike trains of 17,805 neuron pairs were evaluated for short-time scale correlational signatures indicative of functional connectivity with standard cross-correlation analysis, supplemented with gravitational clustering; ∼70% of tested (498 of 704) and responding neurons (333 of 474) were involved in a functional correlation with at least one other cell. Changes in high-frequency oscillations in the spiking of inspiratory neurons and the evocation or resetting of slow quasi-periodic fluctuations in the respiratory motor pattern associated with oscillations of arterial pressure were observed. The results support a linked-loop pontomedullary network architecture for multispectral tuning of inspiration.NEW & NOTEWORTHY The brain network that supports cardiorespiratory coupling remains poorly understood. Using multielectrode arrays, we tested the hypothesis that blood pressure and baroreceptor reflexes "tune" the breathing motor pattern via a raphe-pontomedullary network. Neuron responses to changes in arterial pressure and identified functional connectivity, together with altered high frequency and slow Lundberg B-wave oscillations, support a model with linked recurrent inhibitory loops that stabilize the respiratory network and provide a path for transmission of baroreceptor signals.

Analysis of diagonals in cross recurrence plots between heart rate and systolic blood pressure during supine position and active standing in healthy adults

The inter beat interval (IBI) duration and systolic blood pressure (SBP) are cardiovascular variables related through several feedback mechanisms. We propose the analysis of diagonal lines in cross recurrence plots (CRPs) from IBI and SBP embedded within the same phase space to identify events where trajectories of both variables concur. The aim of the study was to describe the relationship between IBI and SBP of healthy subjects using CRP and diagonal analysis during baseline condition-supine position (SP)-and how the relationship changes during the physiological stress of active standing (AS). IBI and SBP time series were obtained from continuous blood pressure recordings during SP and AS (15 min each) in 19 young healthy subjects. IBI and SBP time series were embedded within a five-dimensional phase space using an embedding delay estimated from cross correlation between IBI and SBP. During SP, mean CRP showed high determinism (≥85%) and also brief but repeated events where both variables stay within a reduced space. Most quantitative recurrences analysis indexes of CRP increased significantly (p < 0.05) during AS. CRP analysis showed short diagonals indicating a very strong deterministic relationship between IBI and SBP with intermittent unlocking periods. The strength of IBI and SBP relationship increased during the physiological stress of AS. The CRP method allowed a rigorous quantitative description of the deterministic association between these two variables. Diagonal lines were intermittent and not always parallel, showing that there is not a defined and unique rhythm. This suggests the activation of different influences at different times and with different precedence between the heart rate and blood pressure in response to AS.

Walking on a Vertically Oscillating Treadmill: Phase Synchronization and Gait Kinematics

Sensory motor synchronization can be used to alter gait behavior. This type of therapy may be useful in a rehabilitative setting, though several questions remain regarding the most effective way to promote and sustain synchronization. The purpose of this study was to describe a new technique for using synchronization to influence a person’s gait and to compare walking behavior under this paradigm with that of side by side walking. Thirty one subjects walked on a motorized treadmill that was placed on a platform that oscillated vertically at various frequencies and amplitudes. Synchronization with the platform and stride kinematics were recorded during these walking trials and compared with previously reported data from side by side walking. The results indicated that vertical oscillation of the treadmill surface at frequencies that matched subjects preferred stride or step frequency resulted in greater unintentional synchronization when compared with side by side walking data (up to 78.6±8.3% of the trial vs 59.2±17.4%). While intermittent phase locking was observed in all cases, periods of synchronization occurred more frequently and lasted longer while walking on the oscillating treadmill (mean length of periods of phase locking 11.85 steps vs 5.18 steps). Further, stride length, height and duration were altered by changing the frequency of treadmill oscillation. These results suggest that synchronization to a haptic signal may hold implications for use in a clinical setting.

Joint symbolic dynamics for the assessment of cardiovascular and cardiorespiratory interactions

Beat-to-beat variations in heart period provide information on cardiovascular control and are closely linked to variations in arterial pressure and respiration. Joint symbolic analysis of heart period, systolic arterial pressure and respiration allows for a simple description of their shared short-term dynamics that are governed by cardiac baroreflex control and cardiorespiratory coupling. In this review, we discuss methodology and research applications. Studies suggest that analysis of joint symbolic dynamics provides a powerful tool for identifying physiological and pathophysiological changes in cardiovascular and cardiorespiratory control.

Embedded grey relation theory in Hopfield neural network: application to motor imagery EEG recognition

In this study, grey-based Hopfield neural network (GHNN), is proposed for the unsupervised analysis of motor imagery (MI) electroencephalogram (EEG) data. Combined with segment selection and feature extraction, GHNN is used for the recognition of left and right MI data. A Gaussian-like filter is proposed to reduce noise, to further enhance performance of active segment selection. Features are extracted by coherence from wavelet data, and then discriminated by GHNN, which is an unsupervised approach suitable for the online classification of nonstationary biomedical signals. Compared to EEG data without segment selection, several usual features, and classifiers, the proposed system is potentially an analytic approach in brain-computer interface (BCI) applications.

Reduction theories elucidate the origins of complex biological rhythms generated by interacting delay-induced oscillations

Time delay is known to induce sustained oscillations in many biological systems such as electroencephalogram (EEG) activities and gene regulations. Furthermore, interactions among delay-induced oscillations can generate complex collective rhythms, which play important functional roles. However, due to their intrinsic infinite dimensionality, theoretical analysis of interacting delay-induced oscillations has been limited. Here, we show that the two primary methods for finite-dimensional limit cycles, namely, the center manifold reduction in the vicinity of the Hopf bifurcation and the phase reduction for weak interactions, can successfully be applied to interacting infinite-dimensional delay-induced oscillations. We systematically derive the complex Ginzburg-Landau equation and the phase equation without delay for general interaction networks. Based on the reduced low-dimensional equations, we demonstrate that diffusive (linearly attractive) coupling between a pair of delay-induced oscillations can exhibit nontrivial amplitude death and multimodal phase locking. Our analysis provides unique insights into experimentally observed EEG activities such as sudden transitions among different phase-locked states and occurrence of epileptic seizures.

Cardiac and respiratory patterns synchronize between persons during choir singing

Dyadic and collective activities requiring temporally coordinated action are likely to be associated with cardiac and respiratory patterns that synchronize within and between people. However, the extent and functional significance of cardiac and respiratory between-person couplings have not been investigated thus far. Here, we report interpersonal oscillatory couplings among eleven singers and one conductor engaged in choir singing. We find that: (a) phase synchronization both in respiration and heart rate variability increase significantly during singing relative to a rest condition; (b) phase synchronization is higher when singing in unison than when singing pieces with multiple voice parts; (c) directed coupling measures are consistent with the presence of causal effects of the conductor on the singers at high modulation frequencies; (d) the different voices of the choir are reflected in network analyses of cardiac and respiratory activity based on graph theory. Our results suggest that oscillatory coupling of cardiac and respiratory patterns provide a physiological basis for interpersonal action coordination.

Time delay correction of the synchrogram for optimized detection of cardiorespiratory coordination

The cardiorespiratory synchrogram, a graphical tool based on the stroboscopic technique, is an established method for evaluating phase-locking between cardiac and respiratory oscillators. In the original method, the phase of the respiratory oscillator is observed at the instants of time when the phase of the cardiac oscillator attains a certain value. In this article, we introduced an additional adaptive delay in the cardiac oscillator based on the maximisation of the cross-correlation or symbolic coupling traces between the phases of respiration and the delayed R-R intervals. We then investigated phase coordination in thirteen normal subjects (five males, eight females; age: 19-24 years) for different body postures. Cardiorespiratory coordination was observed to be significantly reduced in the upright position (supine vs. upright: 11.9 ± 5.1 vs. 6.9 ± 3.6, P < 0.05). Compared to the original algorithm we observed an increase in the detection of average cardiorespiratory coordination (supine original vs. delay: 11.9 vs. 18.9%), together with a decrease in standard deviation of the percentage of coordination in all the subjects, after introducing the heart rate delay (supine original vs. delay: 5.1 vs. 4.4%). In conclusion, the performance of the synchrogram technique was improved by including an adaptive delay in the cardiac oscillator.

Symbolic dynamic analysis of relations between cardiac and breathing cycles in patients on weaning trials

Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure.

Cardiorespiratory phase-coupling is reduced in patients with obstructive sleep apnea

Cardiac and respiratory rhythms reveal transient phases of phase-locking which were proposed to be an important aspect of cardiorespiratory interaction. The aim of this study was to quantify cardio-respiratory phase-locking in obstructive sleep apnea (OSA). We investigated overnight polysomnography data of 248 subjects with suspected OSA. Cardiorespiratory phase-coupling was computed from the R-R intervals of body surface ECG and respiratory rate, calculated from abdominal and thoracic sensors, using Hilbert transform. A significant reduction in phase-coupling was observed in patients with severe OSA compared to patients with no or mild OSA. Cardiorespiratory phase-coupling was also associated with sleep stages and was significantly reduced during rapid-eye-movement (REM) sleep compared to slow-wave (SW) sleep. There was, however, no effect of age and BMI on phase coupling. Our study suggests that the assessment of cardiorespiratory phase coupling may be used as an ECG based screening tool for determining the severity of OSA.

Phase resetting curves allow for simple and accurate prediction of robust N:1 phase locking for strongly coupled neural oscillators

Existence and stability criteria for harmonic locking modes were derived for two reciprocally pulse coupled oscillators based on their first and second order phase resetting curves. Our theoretical methods are general in the sense that no assumptions about the strength of coupling, type of synaptic coupling, and model are made. These methods were then tested using two reciprocally inhibitory Wang and Buzsáki model neurons. The existence of bands of 2:1, 3:1, 4:1, and 5:1 phase locking in the relative frequency parameter space was predicted correctly, as was the phase of the slow neuron's spike within the cycle of the fast neuron in which it occurred. For weak coupling the bands are very narrow, but strong coupling broadens the bands. The predictions of the pulse coupled method agreed with weak coupling methods in the weak coupling regime, but extended predictability into the strong coupling regime. We show that our prediction method generalizes to pairs of neural oscillators coupled through excitatory synapses, and to networks of multiple oscillatory neurons. The main limitation of the method is the central assumption that the effect of each input dies out before the next input is received.

Information flow to assess cardiorespiratory interactions in patients on weaning trials

Nonlinear processes of the autonomic nervous system (ANS) can produce breath-to-breath variability in the pattern of breathing. In order to provide assess to these nonlinear processes, nonlinear statistical dependencies between heart rate variability and respiratory pattern variability are analyzed. In this way, auto-mutual information and cross-mutual information concepts are applied. This information flow analysis is presented as a short-term non linear analysis method to investigate the information flow interactions in patients on weaning trials. 78 patients from mechanical ventilation were studied: Group A of 28 patients that failed to maintain spontaneous breathing and were reconnected; Group B of 50 patients with successful trials. The results show lower complexity with an increase of information flow in group A than in group B. Furthermore, a more (weakly) coupled nonlinear oscillator behavior is observed in the series of group A than in B.

Quantifying cardio-pulmonary correlations using the cross-wavelet transform: validating a correlative method

Dynamic changes in physiologic systems have become an increasingly important topic in biomedical signal processing. To demonstrate a novel approach to this type of analysis, we recorded the cardiac and respiratory rhythms of six normal subjects over 5 minutes. We used cross-wavelet transforms to identify any correlations between these signals and then a unique approach to surrogate data generation in the frequency domain to confirm the statistical significance of the correlations that were found. The cross-wavelet transform provides a means of statistically quantifying weak correlations between systems that might otherwise not show significant interactions, while the method of surrogate data generation is a robust way of confirming a true physiological relationship.

Detection of Nonlinearity in Cardiovascular Variability Signals using Cyclostationary Analysis

A novel approach for detection of polynomial nonlinearity in the neuro-cardiovascular system based on cyclostationary analysis is presented. Metronome breathing is employed to provide a sinusoidal input to the neuro-cardiovascular system in which Heart Rate Variability (HRV) and Blood Pressure Variation (BPV) are considered as its outputs. The presence of new harmonics of the main respiratory rate in the HRV and BPV is investigated by using the concept of (self) phase and (self) frequency coupling. It is shown that a second order polynomial nonlinear system is actually involved in producing the HRV and BPV. The strength of this nonlinearity decreases with increasing the breathing rate.

Analysis of the nonlinear autodependencies of respiratory pattern in patients on weaning trials

Traditional time domain techniques of data analysis are often not sufficient to characterize the nonlinear dynamics of respiration. In this study, the respiratory pattern variability was analyzed using auto mutual information measures. These provide access to nonlinear statistical autodependencies of respiratory pattern variability. A group of 20 patients on weaning trials from mechanical ventilation were studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of breathing duration, inspiratory time, fractional inspiratory time, tidal volume and mean inspiratory flow were analyzed. Different measures based on auto-mutual information were studied to characterize the respiratory pattern variability with regard to its complex organization.

A closed-loop hybrid physiological model relating to subjects under physical stress

OBJECTIVE

The objective of this research study is to derive a comprehensive physiological model relating to subjects under physical stress conditions. The model should describe the behaviour of the cardiovascular system, respiratory system, thermoregulation and brain activity in response to physical workload.

METHODS AND MATERIAL

An experimental testing rig was built which consists of recumbent high performance bicycle for inducing the physical load and a data acquisition system comprising monitors and PCs. The signals acquired and used within this study are the blood pressure, heart rate, respiration, body temperature, and EEG signals. The proposed model is based on a grey-box based modelling approach which was used because of the sufficient level of details it provides. Cardiovascular and EEG Data relating to 16 healthy subject volunteers (data from 12 subjects were used for training/validation and the data from 4 subjects were used for model testing) were collected using the Finapres and the ProComp+ monitors. For model validation, residual analysis via the computing of the confidence intervals as well as related histograms was performed.

RESULTS

Closed-loop simulations for different subjects showed that the model can provide reliable predictions for heart rate, blood pressure, body temperature, respiration, and the EEG signals. These findings were also reinforced by the residual analyses data obtained, which suggested that the residuals were within the 90% confidence bands and that the corresponding histograms were of a normal distribution. A higher intelligent level was added to the model, based on neural networks, to extend the capabilities of the model to predict over a wide range of subjects dynamics.

CONCLUSION

The elicited physiological model describing the effect of physiological stress on several physiological variables can be used to predict performance breakdown of operators in critical environments. Such a model architecture lends itself naturally to exploitation via feedback control in a 'reverse-engineering' fashion to control stress via the specification of a safe operating range for the psycho-physiological variables.

Nonlinear additive autoregressive model-based analysis of short-term heart rate variability

In this contribution we test the hypothesis that nonlinear additive autoregressive model-based data analysis improves the diagnostic ability based on short-term heart rate variability. For this purpose, a nonlinear regression approach, namely, the maximal correlation method is applied to the data of 37 patients with dilated cardiomyopathy as well as of 37 age- and sex-matched healthy subjects. We find that this approach is a powerful tool in discriminating both groups and promising for further model-based analyses.

Optimized Symbolic Dynamics Approach for the Analysis of the Respiratory Pattern

Traditional time domain techniques of data analysis are often not sufficient to characterize the complex dynamics of respiration. In this paper, the respiratory pattern variability is analyzed using symbolic dynamics. A group of 20 patients on weaning trials from mechanical ventilation are studied at two different pressure support ventilation levels, in order to obtain respiratory volume signals with different variability. Time series of inspiratory time, expiratory time, breathing duration, fractional inspiratory time, tidal volume and mean inspiratory flow are analyzed. Two different symbol alphabets, with three and four symbols, are considered to characterize the respiratory pattern variability. Assessment of the method is made using the 40 respiratory volume signals classified using clinical criteria into two classes: low variability (LV) or high variability (HV). A discriminant analysis using single indexes from symbolic dynamics has been able to classify the respiratory volume signals with an out-of-sample accuracy of 100%.

Proposed Corrections for the Quantification of Coupling Patterns by Recurrence Plots

The aim of this paper is to quantify the coupling during phase-locking patterns by using the recurrence plot quantification approach. We found that the percent determinism of the recurrences and the entropy of recurrences--corrected for border effect induced by finite data lenght--succeeded in distinguishing three coupling conditions both in simulation signals and in real data from cardiorespiratory synchronization experiments.

Variability analysis of the respiratory volume based on non-linear prediction methods

This work proposed and studied a method of automatically classifying respiratory volume signals as high or low variability by means of non-linear analysis of the respiratory volume. The analysis used volume signals generated by the respiratory system to construct a model of its dynamics and to estimate the quality of the predictions made with the model. Different methods of prediction evaluation, prediction horizons and embedding dimensions were also analysed. Assessment of the method was made using a database that contained 40 respiratory volume signals classified using clinical criteria into two classes: low or high variability. The results obtained using the method of surrogate data provided evidence of non-linear determinism in the respiratory volume signals. A discriminant analysis carried out using non-linear prediction variables classified the respiratory volume signals with an accuracy of 95%.

Microvascular blood flow and skin temperature changes in the fingers following a deep nspiratory gasp

The aims of this study were to quantify the changes in finger pulp skin temperature, laser Doppler flow (LDF, microvascular flux) and photoplethysmogram (PPG, microvascular blood volume pulsatility), induced by a deep inspiration in healthy subjects, and to investigate the repeatability of these responses within a measurement session and between measurement sessions on separate days. A system comprising an electronic thermometer, a laser Doppler flowmeter and a PPG amplifier measured simultaneous vasoconstrictor responses to a deep inspiratory gasp from three adjacent fingers of one hand. Clearly defined responses were obtained in 15 of the 17 subjects studied. Skin temperature fell in all of these subjects after each gasp, with a median fall of 0.089 degrees C (P < 0.001). The median value of LDF flux reduction was 93% (P < 0.001) indicating a momentary almost complete shut-down of microvascular blood flow; and PPG also showed a large response relative to pulse amplitude of 2.6 (P < 0.001). The median times for waveforms to reach their minimum were 4.6 s (PPG), 6.3 s (LDF) and 29.1 s (skin pulp temperature), with median delays between minima of LDF and PPG of 1.6 s (P < 0.001) and skin temperature and PPG of 23.5 s (P < 0.001). The vascular responses of skin temperature, LDF and PPG to an inspiratory gasp were repeatable, with temperature change repeatable to within 10% of the median subject change.

Joint symbolic dynamic analysis of beat-to-beat interactions of heart rate and systolic blood pressure in normal pregnancy

Pregnancy induces important changes in the autonomic control. Measures of heart rate (HR) variability and systolic blood pressure (SP) variability are sensitive to those changes. The interactions between HR and SP are complex and strongly non-linear. Therefore they cannot be completely described by linear analysis techniques. A study of joint symbolic dynamics is presented as a new short-term non-linear analysis method to investigate the interactions between HR and SP. Continuous, non-invasive 30 min blood pressure recordings (Portapres) of 25 pregnant and 14 non-pregnant women were analysed. Time series of beat-to-beat HR and SP were extracted. Using the concept of joint symbolic dynamics, HR and SP changes were transformed into a bivariate symbol vector. Subsequently, this symbol vector was transformed into a word series (words consisting of three successive symbols), and the probability of occurrence of each word type was calculated and compared between both groups. Significant differences were found in five word types between pregnant and non-pregnant women: w0,4(0.021+/-0.011 against 0.008+/-0.006; p = 0.022), w4,6(0.020+/-0.010 against 0.007+/-0.003; p = 0.001), w3,2(0.004+/-0.003 against 0.007+/-0.003; p = 0.038), W6,5(0.009+/-0.007 against 0.023+/-0.008; p< 0.001) and w3,6(0.011+/-0.007 against 0.023+/-0.008; p = 0.001). Joint symbolic dynamics provides an efficient non-linear representation of HR and SP interactions that offers simple physiological interpretations.