One-dimensional, nonlinear determinism characterizes heart rate pattern during paced respiration

NurseNet: Monitoring Elderly Levels of Activity with a Piezoelectric Floor

Monitoring the activity of elderly individuals in nursing homes is key, as it has been shown that physical activity leads to significant health improvement. In this work, we introduce NurseNet, a system that combines an unobtrusive, affordable, and robust piezoelectric floor sensor with a convolutional neural network algorithm, which aims at measuring elderly physical activity. Our algorithm is trained using signal embedding based on atoms of a pre-learned dictionary and focuses the network's attention on step-related signals. We show that NurseNet is able to avoid the main limitation of floor sensors by recognizing relevant signals (i.e., signals produced by patients) and ignoring events related to the medical staff, offering a new tool to monitor elderly activity in nursing homes efficiently.

The logic behind neural control of breathing pattern

The respiratory rhythm generator is spectacular in its ability to support a wide range of activities and adapt to changing environmental conditions, yet its operating mechanisms remain elusive. We show how selective control of inspiration and expiration times can be achieved in a new representation of the neural system (called a Boolean network). The new framework enables us to predict the behavior of neural networks based on properties of neurons, not their values. Hence, it reveals the logic behind the neural mechanisms that control the breathing pattern. Our network mimics many features seen in the respiratory network such as the transition from a 3-phase to 2-phase to 1-phase rhythm, providing novel insights and new testable predictions.

Heart rate variability as determinism with jump stochastic parameters

We use measured heart rate information (RR intervals) to develop a one-dimensional nonlinear map that describes short term deterministic behavior in the data. Our study suggests that there is a stochastic parameter with persistence which causes the heart rate and rhythm system to wander about a bifurcation point. We propose a modified circle map with a jump process noise term as a model which can qualitatively capture such this behavior of low dimensional transient determinism with occasional (stochastically defined) jumps from one deterministic system to another within a one parameter family of deterministic systems.

Evaluating the physiological significance of respiratory sinus arrhythmia: looking beyond ventilation-perfusion efficiency

We conducted a theoretical study of the physiological significance of respiratory sinus arrhythmia (RSA), a phenomenon used as an index of cardiac vagal tone and wellbeing, whereby the heart rate (HR) increases during inspiration and decreases during expiration. We first tested the hypothesis that RSA improves gas exchange efficiency but found that although gas exchange efficiency improved with slow and deep breathing and with increased mean heart rate, this was unrelated to RSA. We then formulated and tested a new hypothesis: that RSA minimizes the work done by the heart while maintaining physiological levels of arterial carbon dioxide. We tested the new hypothesis using two methods. First, the HR for which the work is minimized was calculated using techniques from optimal control theory. This calculation was done on simplified models that we derived from a previously published model of gas exchange in mammals. We found that the calculated HR was remarkably similar to RSA and that this became more profound under slow and deep breathing. Second, the HR was prescribed and the work done by the heart was calculated by conducting a series of numerical experiments on the previously published gas exchange model. We found that cardiac work was minimized for RSA-like HR functions, most profoundly under slow and deep breathing. These findings provide novel insights into potential reasons for and benefits of RSA under physiological conditions.

Online cardiac arrhythmia classification by means of circle maps analysis implemented on an intelligent miniaturized sensor

The intermittent occurrence of cardiac arrhythmias like e.g. atrial fibrillation hampers their diagnosis and hence the treatment. Since persons suffering from atrial fibrillation are known to have a remarkable increased risk of stroke the diagnosis of atrial fibrillation is a matter of great importance. Easy and comfortable to use long term ECG recording systems capable of online arrhythmia classification might help to solve this problem. We developed an intelligent, miniaturized, and wireless networking sensor which allows lossless local data recordings up to 4 GB. With its outer dimensions of 20mm per rim and less than 15g of weight including the Lithium-Ion battery our modular designed sensor node is thoroughly capable of up to eight channel recordings with 8 kHz sample rate each and provides sufficient computational power for online digital signal processing. For online arrhythmia classification we will record one ECG channel and 3-axis accelerometer data with 512 Hz each, the later being used for activity classification based artifact identification. We adapted our recently developed circle maps analysis of short term heart rate variation to run on this miniaturized intelligent sensor powered by the Texas Instruments MSP430 microcontroller derivate F1611. With this configuration we started to evaluate the cardiac arrhythmia classification in long term ECG recordings.

Nonlinear statistical modeling and model discovery for cardiorespiratory data

We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modeling from blood pressure time-series data. The technique is applicable to a broad range of stochastic dynamical models and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using model-generated data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.

Simplified models for gas exchange in the human lungs

This paper presents a hierarchy of models with increasing complexity for gas exchange in the human lungs. The models span from a single compartment, inflexible lung to a single compartment, flexible lung with pulmonary gas exchange. It is shown how the models are related to well-known models in the literature. A long-term purpose of this work is to study nonlinear phenomena seen in the cardio-respiratory system (for example, synchronization between ventilation rate and heart rate, and Cheyne-Stokes respiration). The models developed in this paper can be regarded as the controlled system (plant) and provide a mathematical framework to link between "molecular-level", and "systems-level" models. It is shown how changes in molecular level affect the alveolar partial pressure. Two assumptions that have previously been made are re-examined: (1) the hidden assumption that the air flow through the mouth is equal to the rate of volume change in the lungs, and, (2) the assumption that the process of oxygen binding to hemoglobin is near equilibrium. Conditions under which these assumptions are valid are studied. All the parameters in the models, except two, are physiologically realistic. Numerical results are consistent with published experimental observations.

Inference of a nonlinear stochastic model of the cardiorespiratory interaction

We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.

Time-phase bispectral analysis

Bispectral analysis, a technique based on high-order statistics, is extended to encompass time dependence for the case of coupled nonlinear oscillators. It is applicable to univariate as well as to multivariate data obtained, respectively, from one or more of the oscillators. It is demonstrated for a generic model of interacting systems whose basic units are the Poincaré oscillators. Their frequency and phase relationships are explored for different coupling strengths, both with and without Gaussian noise. The distinctions between additive linear or quadratic, and parametric (frequency modulated), interactions in the presence of noise are illustrated.

Comparison of two different approaches in the detection of intermittent cardiorespiratory coordination during night sleep

BACKGROUND

The objective was to evaluate and to compare two completely different detection algorithms of intermittent (short-term) cardiorespiratory coordination during night sleep. The first method is based on a combination of respiratory flow and electrocardiogram recordings and determines the relative phases of R waves between successive onsets of inspiration. Intermittent phase coordination is defined as phase recurrence with accuracy alpha over at least k heartbeats. The second, recently introduced method utilizes only binary coded variations of heart rate (acceleration = 1, deceleration = 0) and identifies binary pattern classes which can be assigned to respiratory sinus arrhythmia (RSA). It is hypothesized that RSA pattern class recurrence over at least k heartbeats is strongly related with the intermittent phase coordination defined above.

RESULTS

Both methods were applied to night time recordings of 20 healthy subjects. In subjects <45 yrs and setting k = 3 and alpha = 0.03, the phase and RSA pattern recurrence were highly correlated. Furthermore, in most subjects the pattern predominance (PP) showed a pronounced oscillation which is most likely linked with the dynamics of sleep stages. However, the analysis of bivariate variation and the use of surrogate data suggest that short-term phase coordination mainly resulted from central adjustment of heart rate and respiratory rate rather than from real phase synchronization due to physiological interaction.

CONCLUSION

Binary pattern analysis provides essential information on short-term phase recurrence and reflects nighttime sleep architecture, but is only weakly linked with true phase synchronization which is rare in physiological processes of man.

Preserved parasympathetic cardiac innervation after atrioventricular node modification: evidence from circle maps of respiratory sinus arrhythmia

INTRODUCTION

Respiratory sinus arrhythmia (RSA) and heart rate variability (HRV) are parameters of autonomic cardiac innervation. They decrease with age and after atrioventricular nodal modification (AVNM) suggesting vagal denervation in both situations. We hypothesized, however, that AVNM causes only a transient, functional decline in vagal activity, whereas aging causes permanent vagal denervation. A new method of analyzing RSA phase dynamics based on circle maps (CM) can potentially differentiate between both forms of reduced vagal activity.

METHODS

In 18 younger and 14 older healthy control subjects 24-hour Holter ECGs were recorded for HRV analysis. Repeated measurements of RSA were acquired during paced breathing (PB). In 16 consecutive patients undergoing AVNM the same measurements were applied before, 1 day and 3 months after the procedure. CM were calculated from consecutive RR intervals and the similarity between different CM quantified by the Kullback information gain (KIG).

RESULTS

HRV analysis revealed lower HF bands, LF bands and RSA amplitudes in older vs. younger control subjects. KIG revealed less similarity between younger and older control subjects than within the respective age groups. After AVNM a decrease in HF bands was noted in HRV analysis. Three months after AVNM, HF bands returned to pre-ablation values. CM obtained before and 1 day after AVNM displayed comparable similarity to CM acquired 1 day before and 3 months after ablation.

CONCLUSIONS

In contrast to conventional HRV parameters, CM of RSA are not altered by ablation in the posteroseptal space but by aging. Thus, this new method appears to differentiate between transient autonomic modification and chronic denervation.

Phase relationships between two or more interacting processes from one-dimensional time series. II. Application to heart-rate-variability data

The recently proposed approach to detect synchronization from univariate data is applied to heart-rate-variability (HRV) data from ten healthy humans. The approach involves introducing angles for return times map and studying their behavior. For filtered human HRV data, it is demonstrated that: (i) in many of the subjects studied, interactions between different processes within the cardiovascular system can be considered as weak, and the angles can be well described by the derived model; (ii) in some of the subjects the strengths of the interactions between the processes are sufficiently large that the angles map has a distinctive structure, which is not captured by our model; (iii) synchronization between the processes involved can often be detected; (iv) the instantaneous radii are rather disordered.