Recurrence plots for dynamical analysis of non-invasive mechanical ventilation

COPD Patients Have a Restricted Breathing Pattern That Persists with Increased Metabolic Demands

A healthy respiratory system has variability from breath-to-breath and patients with COPD (PwCOPD) have abnormal variability in breath cycles. The aim of this study was to determine if interbreath-interval and tidal-volume variability, and airflow regularity change as metabolic demands increase (seated, standing, and walking) in PwCOPD as compared to controls. Sixteen PwCOPD (64.3 ± 7.9 yr, 61.3 ± 44.1% FEV₁%predicted) and 21 controls (60.2 ± 6.8 yr, 97.5 ± 16.8% FEV₁%predicted) sat, stood, and walked at their preferred-pace for five-minutes each while breathing patterns were recorded. The mean, standard deviation, and coefficient of variation of interbreath-intervals and tidal-volume, and the regularity (sample entropy) of airflow were quantified. Results were subjected to ANOVA analysis. Interbreath-interval means were shorter in PwCOPD compared to controls (p = 0.04) and as metabolic demand increased (p < 0.0001), standard deviation was decreased in PwCOPD compared to controls during each condition (p's < 0.002). Mean tidal-volume did decrease as metabolic demand increased across groups (p < 0.0001). Coefficient of variation findings (p = 0.002) indicated PwCOPD decline in tidal-volume variability from sitting to standing to walking; whereas, controls do not. There was an interaction for airflow (p = 0.02) indicating that although, PwCOPD had a more regular airflow across all conditions, control's airflow became more irregular as metabolic demand increased. PwCOPD's airflow was always more regular compared to controls (p = 0.006); although, airflow became more irregular as metabolic demand increased (p < 0.0001). Healthy respiratory systems have variability and irregularity from breath-to-breath decreases with adaptation to demand. PwCOPD have more regular and restricted breathing pattern that may affect their ability to adjust in demanding situations.

Entropy change of biological dynamics in COPD

In this century, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of large amount of data in human physiological signals. Entropy is a key metric for quantifying the irregularity contained in physiological signals. In this review, we focus on how entropy changes in various physiological signals in COPD. Our review concludes that the entropy change relies on the types of physiological signals under investigation. For major physiological signals related to respiratory diseases, such as airflow, heart rate variability, and gait variability, the entropy of a patient with COPD is lower than that of a healthy person. However, in case of hormone secretion and respiratory sound, the entropy of a patient is higher than that of a healthy person. For mechanomyogram signal, the entropy increases with the increased severity of COPD. This result should give valuable guidance for the use of entropy for physiological signals measured by wearable medical device as well as for further research on entropy in COPD.

Complexity in congestive heart failure: A time-frequency approach

Reconstruction of phase space is an effective method to quantify the dynamics of a signal or a time series. Various phase space reconstruction techniques have been investigated. However, there are some issues on the optimal reconstructions and the best possible choice of the reconstruction parameters. This research introduces the idea of gradient cross recurrence (GCR) and mean gradient cross recurrence density which shows that reconstructions in time frequency domain preserve more information about the dynamics than the optimal reconstructions in time domain. This analysis is further extended to ECG signals of normal and congestive heart failure patients. By using another newly introduced measure-gradient cross recurrence period density entropy, two classes of aforesaid ECG signals can be classified with a proper threshold. This analysis can be applied to quantifying and distinguishing biomedical and other nonlinear signals.

Vagal-dependent nonlinear variability in the respiratory pattern of anesthetized, spontaneously breathing rats

Physiological rhythms, including respiration, exhibit endogenous variability associated with health, and deviations from this are associated with disease. Specific changes in the linear and nonlinear sources of breathing variability have not been investigated. In this study, we used information theory-based techniques, combined with surrogate data testing, to quantify and characterize the vagal-dependent nonlinear pattern variability in urethane-anesthetized, spontaneously breathing adult rats. Surrogate data sets preserved the amplitude distribution and linear correlations of the original data set, but nonlinear correlation structure in the data was removed. Differences in mutual information and sample entropy between original and surrogate data sets indicated the presence of deterministic nonlinear or stochastic non-Gaussian variability. With vagi intact (n = 11), the respiratory cycle exhibited significant nonlinear behavior in templates of points separated by time delays ranging from one sample to one cycle length. After vagotomy (n = 6), even though nonlinear variability was reduced significantly, nonlinear properties were still evident at various time delays. Nonlinear deterministic variability did not change further after subsequent bilateral microinjection of MK-801, an N-methyl-D-aspartate receptor antagonist, in the Kölliker-Fuse nuclei. Reversing the sequence (n = 5), blocking N-methyl-D-aspartate receptors bilaterally in the dorsolateral pons significantly decreased nonlinear variability in the respiratory pattern, even with the vagi intact, and subsequent vagotomy did not change nonlinear variability. Thus both vagal and dorsolateral pontine influences contribute to nonlinear respiratory pattern variability. Furthermore, breathing dynamics of the intact system are mutually dependent on vagal and pontine sources of nonlinear complexity. Understanding the structure and modulation of variability provides insight into disease effects on respiratory patterning.

Biosignatures of health

The term health is commonplace in both everyday parlance and professional discourse. Unfortunately, the term has little objective specification, especially in physiologic terms. When critically examined, even time-honored terms such as homeostasis lack specific measurable referents. The last three decades, however, have witnessed an explosion of information from diverse fields regarding the dynamical basis of biology. This brief review explores a few main ideas, which appear to be coming together to provide biosignatures of health.

Monitoring Patient/Ventilator Interactions: Manufacturer's Perspective

The introduction of reduced and more powerful electronics has allowed the transition of medical equipment such as respiratory support devices from the hospital to the patient’s home environment. Even if this move could be beneficial for the patient, the clinician ends up in a delicate situation where little or no direct supervision is possible on the delivered treatment.

Progress in technologies led to an improved handling of patient-device interaction: manufacturers are promoting new or improved ventilation modes or cycling techniques for better patient-ventilator coupling. Even though these ventilation modes have become more responsive to patient efforts, adversely they might lead to events such as false triggering, autotriggering, delayed triggering.

In addition, manufacturers are developing tools to enhance the follow-up, remotely or offline, of the treatment by using embedded memory in the respiratory devices. This logging might be beneficial for the caregiver to review and document the treatment and tune the settings to the patient’s need and comfort. Also, remote telemedicine has been raised as a potential solution for many years without yet overall acceptance due to legal, technical and ethical problems.

Benefits of new technologies in respiratory support devices give the technical foundation for the transition from hospital to home and reducing patient/ventilator asynchronies. Healthcare infrastructure has to follow this trend in terms of cost savings versus hospital stays.