Oscillations in biological signal

This thesis is a study of electrocardiography, blood pressure and glucose signals in the search for oscillatory physiological processes that are of importance for resilience and the development of disease. An oscillation is defined as a repetitive variation about a central value. Physiology shows oscillating behaviour over large spatial and temporal scales – from rapid variations of proteins and electrolytes on a cellular level to the female hormone cycle ranging over weeks and affecting the whole organism. The oscillatory processes are tightly linked to regulatory mechanisms, and several oscillations identified in biological signals are shown to be direct results of autonomic regulation. The loss of biological oscillations reduces the complexity of biological systems, which is seen with ageing and disease. It is believed that information about biological oscillations, if correctly extracted from biological signals, can be used in patient monitoring, diagnostics and prognostics. In the first paper, we explore three different analyses’ capabilities for identifying oscillatory components in a blood pressure signal. We focus on time-frequency analyses, which capture the time-variability of the oscillations, and we illustrate how such analyses have different temporal resolution among low frequencies. In the second paper, we analyse glucose recordings from pigs, identifying a previously not reported oscillation with frequency 0.01-0.02 Hz. Further, we observe that the oscillations are not constantly present, but rather come and go. In the third and fourth papers, we analyse time series of heart rate, systolic blood pressure and R-wave amplitude in healthy and cardiac surgery patients, respectively. We identify oscillatory components in all variables and subjects, showing large interindividual variations. In paper three, we identify slow oscillations in R-wave amplitude and illustrate cases where they are synchronized with oscillations in systolic blood pressure and heart rate. In paper four, we do not see distinct changes in the oscillatory distributions after cardiac surgery. The overall conclusion of this thesis is that the oscillatory distributions of electrocardiography and blood pressure signals of healthy and cardiac surgery patients are highly heterogenous and do not hold features that are either group-specific or common for both groups. Hence, we have not been able to identify information suitable for implementation in clinical decision tools. We are doubtful regarding biological oscillations’ capability of solely providing such valuable information. Consequently, there are major technological challenges that need to be overcome before automated tools are ready for clinical use

A pig model of acute right ventricular afterload increase by hypoxic pulmonary vasoconstriction

Background: The aim of this study was to construct a non-invasive model for acute right ventricular afterload increase by hypoxic pulmonary vasoconstriction. Intact animal models are vital to improving our understanding of the pathophysiology of acute right ventricular failure. Acute right ventricular failure is caused by increased afterload of the right ventricle by chronic or acute pulmonary hypertension combined with regionally or globally reduced right ventricular contractile capacity. Previous models are hampered by their invasiveness; this is unfortunate as the pulmonary circulation is a low-pressure system that needs to be studied in closed chest animals. Hypoxic pulmonary vasoconstriction is a mechanism that causes vasoconstriction in alveolar vessels in response to alveolar hypoxia. In this study we explored the use of hypoxic pulmonary vasoconstriction as a means to increase the pressure load on the right ventricle. Results: Pulmonary hypertension was induced by lowering the FiO2 to levels below the physiological range in eight anesthetized and mechanically ventilated pigs. The pigs were monitored with blood pressure measurements and blood gases. The mean pulmonary artery pressures (mPAP) of the animals increased from 18.3 (4.2) to 28.4 (4.6) mmHg and the pulmonary vascular resistance (PVR) from 254 (76) dyns/cm5 to 504 (191) dyns/cm5 , with a lowering of FiO2 from 0.30 to 0.15 (0.024). The animals’ individual baseline mPAPs varied substantially as did their response to hypoxia. The reduced FiO2 level yielded an overall lowering in oxygen offer, but the global oxygen consumption was unaltered. Conclusions: We showed in this study that the mPAP and the PVR could be raised by approximately 100% in the study animals by lowering the FiO2 from 0.30 to 0.15 (0.024). We therefore present a novel method for minimally invasive (closed chest) right ventricular afterload manipulations intended for future studies of acute right ventricular failure. The method should in theory be reversible, although this was not studied in this work. Keywords: Acute right ventricular failure, Pulmonary hypertension, Hypoxic pulmonary vasoconstrictio

A pig model of acute right ventricular afterload increase by hypoxic pulmonary vasoconstriction

Background: The aim of this study was to construct a non-invasive model for acute right ventricular afterload increase by hypoxic pulmonary vasoconstriction. Intact animal models are vital to improving our understanding of the pathophysiology of acute right ventricular failure. Acute right ventricular failure is caused by increased afterload of the right ventricle by chronic or acute pulmonary hypertension combined with regionally or globally reduced right ventricular contractile capacity. Previous models are hampered by their invasiveness; this is unfortunate as the pulmonary circulation is a low-pressure system that needs to be studied in closed chest animals. Hypoxic pulmonary vasoconstriction is a mechanism that causes vasoconstriction in alveolar vessels in response to alveolar hypoxia. In this study we explored the use of hypoxic pulmonary vasoconstriction as a means to increase the pressure load on the right ventricle. Results: Pulmonary hypertension was induced by lowering the FiO2 to levels below the physiological range in eight anesthetized and mechanically ventilated pigs. The pigs were monitored with blood pressure measurements and blood gases. The mean pulmonary artery pressures (mPAP) of the animals increased from 18.3 (4.2) to 28.4 (4.6) mmHg and the pulmonary vascular resistance (PVR) from 254 (76) dyns/cm5 to 504 (191) dyns/cm5, with a lowering of FiO2 from 0.30 to 0.15 (0.024). The animals’ individual baseline mPAPs varied substantially as did their response to hypoxia. The reduced FiO2 level yielded an overall lowering in oxygen offer, but the global oxygen consumption was unaltered. Conclusions: We showed in this study that the mPAP and the PVR could be raised by approximately 100% in the study animals by lowering the FiO2 from 0.30 to 0.15 (0.024). We therefore present a novel method for minimally invasive (closed chest) right ventricular afterload manipulations intended for future studies of acute right ventricular failure. The method should in theory be reversible, although this was not studied in this work

Cardiac surgery does not lead to loss of oscillatory components in circulatory signals

The circulatory system is oscillatory in its nature. Oscillatory components linked to physiological processes and underlying regulatory mechanisms are identifiable in circulatory signals. Autonomic regulation is essential for the system's ability to deal with external exposure, and the integrity of oscillations may be considered a hallmark of a healthy system. Loss of complexity is seen as a consequence of several diseases and aging. Heart rate variability is known to decrease after cardiac surgery and remain reduced for up to 6 months. Oscillatory components of circulatory signals are linked to the system's overall complexity. We therefore hypothesize that the frequency distributions of circulatory signals show loss of oscillatory components after cardiac surgery and that the observed changes persist. We investigated the development of the circulatory frequency distributions of eight patients undergoing cardiac surgery by extracting three time series from conventional blood pressure and electrocardiography recordings: systolic blood pressure, heart rate, and amplitude of the electrocardiogram's R‐wave. Four 30‐min selections, representing key events of the perioperative course, were analyzed with the continuous wavelet transform, and average wavelet power spectra illustrated the circulatory frequency distributions. We identified oscillatory components in all patients and variables. Contrary to our hypothesis, they were randomly distributed through frequencies, patients, and situations, thus, not representing any reduction in the overall complexity. One patient showed loss of a 25‐s oscillation after surgery. We present a case where noise is misclassified as an oscillation, raising questions about the robustness of such analyses

MOESM1 of A pig model of acute right ventricular afterload increase by hypoxic pulmonary vasoconstriction

Additional file 1: Table S1. Arterial and mixed venous blood gases at baseline and pulmonary hypertension

Some oscillatory phenomena of blood glucose regulation: An exploratory pilot study in pigs

It is well-known that blood glucose oscillates with a period of approximately 15 min (900 s) and exhibits an overall complex behaviour in intact organisms. This complexity is not thoroughly studied, and thus, we aimed to decipher the frequency bands entailed in blood glucose regulation. We explored high-resolution blood glucose time-series sampled using a novel continuous intravascular sensor in four pigs under general anaesthesia for almost 24 hours. In all time series, we found several interesting oscillatory components, especially in the 5000–10000 s, 500–1000 s, and 50–100 s regions (0.0002–0.0001 Hz, 0.002–0.001 Hz, and 0.02–0.01 Hz). The presence of these oscillations is not permanent, as they come and go. This is the first report of glucose oscillations in the 50–100 s range. The origin of these oscillations and their role in overall blood glucose regulation is unknown. Although the sample size is small, we believe this finding is important for our understanding of glucose regulation and perhaps for our understanding of general homeostatic regulation in intact organisms.publishedVersion© 2018 Skjaervold et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

A period from Pig 2 in which two different oscillatory components follows each other.

<p>In Fig 7A, the 500–1000-sec slow oscillating component in the second half of the time series is shown. A period (marked in red, enlarged in panel B) seems to have a fast oscillating component at 50–100 sec. The wavelet power spectrum from the continuous wavelet transform of the time series in 7 B is displayed in 7 C, and this clearly shows the 50–100 sec oscillatory component.</p

Some interesting periods from Pig 1 with oscillations in the 50–100 sec period range.

<p>Some interesting periods from Pig 1 with oscillations in the 50–100 sec period range.</p

Some interesting periods from Pig 4 with oscillations in the 50–100-sec range and the 1000-sec range.

<p>Some interesting periods from Pig 4 with oscillations in the 50–100-sec range and the 1000-sec range.</p

The fractal nature of blood glucose oscillations illustrated with two examples from Pig 3.

<p>Panels A and D depicts the time series from two situations in which the 1000-sec oscillation is clearly seen. Panels B and E depicts the wavelet power spectrum from the continuous wavelet transform from A and D, respectively, clearly showing the 1000-sec oscillatory component. However, the 50–100-sec component is poorly depicted in these figures due to the low power in the high-frequency oscillations compared with the low-frequency oscillations. Thus, panels C and F depicts the 50–100-sec components from A and D, respectively. (BGL = blood glucose level).</p