Limits to the usefulness of homeostasis as a guiding physiological principle are revealed by new mechanisms derived from study of nonlinear systems that generate a type of variability called chaos. Loss of complex physiological variability may occur in certain pathological conditions including heart rate dynamics before sudden death and with aging.
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... The developmental process of an organism (ontogenesis) with phase transitions between different attractors (37) is characterized by an increase of complexity. Vice versa, in medicine, we find many diseases with a decrease of complexity (38,39). The following example of heart diseases (39) is shown to explain the analytical methods, which use the change of the regularity in time series to characterize this change in the complexity of the system (40). ...
... With a corresponding lower stability of the state, this analysis could also be regarded for a risk prognosis. The fatal meaning of this comment was seen in the case of the patient with the heart disease in Figure 4B who died 2 weeks after this investigation in an uncontrolled moment from a letal chamber fibrillation (39). ...
... Heart Rate After Delay, bpmHeart Rate After Delay, bpm Heart Rate After Delay, bpm This is is modified from(39). The upper row shows the time series and the lower row the corresponding phase portraits (attractors) in return plots(39). ...
... El cuerpo humano requiere de la interacción de diferentes sistemas, donde se denota en experimentos controlados, que, si un factor cambia, todos los demás tratan de estabilizarse tanto como le sea posible (Ernst, 2017). Se sabe que los sistemas biológicos tienen una alta complejidad espacial y temporal que solo se presentan en sistemas saludables, que presentan un caos con entornos inciertos y variables (Beckers et al., 2006;Eskov et al., 2017;Goldberger, 1991;Vaillancourt & Newell, 2002), por lo que la homeostasis o regulación homeostática, no es en realidad un orden, sino un sistema complejo de procesos para la estabilización de sistemas y subsistemas orgánicos (Eskov et al., 2017). Entre estos sistemas se encuentra la neurotransmisión (Sarbadhikari & Chakrabarty, 2001), los procesos del SNA (Waldbuerger & Firestein, 2010), así como la regulación cardiovascular (Goldberger, 1991) e inmunitaria (Canabarro et al., 2004;Waldbuerger & Firestein, 2010). ...
... Se sabe que los sistemas biológicos tienen una alta complejidad espacial y temporal que solo se presentan en sistemas saludables, que presentan un caos con entornos inciertos y variables (Beckers et al., 2006;Eskov et al., 2017;Goldberger, 1991;Vaillancourt & Newell, 2002), por lo que la homeostasis o regulación homeostática, no es en realidad un orden, sino un sistema complejo de procesos para la estabilización de sistemas y subsistemas orgánicos (Eskov et al., 2017). Entre estos sistemas se encuentra la neurotransmisión (Sarbadhikari & Chakrabarty, 2001), los procesos del SNA (Waldbuerger & Firestein, 2010), así como la regulación cardiovascular (Goldberger, 1991) e inmunitaria (Canabarro et al., 2004;Waldbuerger & Firestein, 2010). Estos cuatro mecanismos están en constante comunicación por medios de procesos neurológicos y de retroalimentación en el que se involucra las estructuras superiores del cerebro y el tronco encefálico (Waldbuerger & Firestein, 2010), para posteriormente enviar señales desde la medula ósea mediante acciones antidrómicas por medio de fibras eferentes (Rees et al., 1994). ...
The moderate-high intensity repetitive exercise may cause fatigue. Although there are several internal load methods to control it, one would not be enough to measure the diverse systems of homeostasis recovery. However, the cholinesterases (ChE) are involved in many of these processes, therefore the objective was to analyze the influence of the cholinesterases behavior in mechanisms of recovery of inflammatory response and post-exercise autonomic nervous system (ANS). Nine volleyball players who participated in this study were subjected to a moderate-high intensity exercise protocol. Basal measurements were done, one after the exercise and one 24 hours of heart rate variability (HRV) and of blood samples for ChE, creatine kinase, interleu-kin-6 (IL-6), interleukin-10 (IL-10) and the tumor necrosis factor-alpha (TNF-a). The results showed significant changes (p < .05) on the subsequent sample of HRV rates, in conjunction with the 24 hours for IL-6 and ChE. Moreover, moderated and large correlations were observed (p < .05) of ChE with HRV rates, large with IL-6 (r = .696; p < .001) and almost perfect (r > .900; p < .001) among the HRV rates. In conclusion, in similar conditions to this study, ChE are possibly involved in homeostasis mechanisms since they are related to ANS, since it provide acceptable information with HRV rates, in addition, it also seems to have an acceptable relation with IL-6, perhaps for its cholinergic pathway connection.
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... HRV analysis is based on the inter-beat interval (RRI) variation calculated from Electrocardiography (ECG), whereas PRV analysis is based on the pulse-to-pulse interval (PPI) variation calculated from PPG[10]. Some studies have reported a chaotic heart beat behaviour[11,12]suggesting that measures describing nonlinear dynamics of heart rate, such as fractal measures, may reveal prognostic information beyond that obtained by conventional measures[6,10,13]. Several studies have proposed the use of entropy methods, i.e., sample entropy[14]and multiscale entropy[6,15], as measures of complexity to differentiate between the HRV pattern of normal and OSA subjects. ...
... Also, the multivariate logistic regression model developed using features extracted from CSD-based analysis provided better classification results, in terms of accuracy, sensitivity, specificity and AUC, than the model created with PSD-based features. The reason for this improvement could be the capability of the correntropy, and subsequently CSD, to preserve nonlinear characteristics and high-order moments of the data[16,28,29], in this case PPIs, which have been previously documented as chaotic or nonlinear[11,12]. In addition, we justified the use of CSD by demonstrating that the majority of the patients showed evidence of nonlinear PPIs behaviour. ...
Pulse rate variability (PRV), an alternative measure of heart rate variability (HRV), is altered during obstructive sleep apnea. Correntropy spectral density (CSD) is a novel spectral analysis that includes nonlinear information. We recruited 160 children and recorded SpO2 and photoplethysmography (PPG), alongside standard polysomnography. PPG signals were divided into 1-min epochs and apnea/hypoapnea (A/H) epochs labeled. CSD was applied to the pulse-to-pulse interval time series (PPIs) and five features extracted: the total spectral power (TP: 0.01-0.6 Hz), the power in the very low frequency band (VLF: 0.01-0.04 Hz), the normalized power in the low and high frequency bands (LFn: 0.04-0.15 Hz, HFn: 0.15-0.6 Hz), and the LF/HF ratio. Nonlinearity was assessed with the surrogate data technique. Multivariate logistic regression models were developed for CSD and power spectral density (PSD) analysis to detect epochs with A/H events. The CSD-based features and model identified epochs with and without A/H events more accurately relative to PSD-based analysis (area under the curve (AUC) 0.72 vs. 0.67) due to the nonlinearity of the data. In conclusion, CSD-based PRV analysis provided enhanced performance in detecting A/H epochs, however, a combination with overnight SpO2 analysis is suggested for optimal results.
... More specifically, the activity of the heart is not regular/periodic, but rather fluctuates in complex/aperiodic patterns. Thus, it has frequently been argued that non-linear measures (i.e., measures of mathematical chaos/entropy) might be more appropriate for the analysis of HRV data [48,49]. This is in line with findings from a study that showed a difference in HRV entropy between patients with UWS and healthy individuals (i.e., lower ApEn in patients with UWS), but no such differences in any of the linear parameters (i.e., IBI, SDRR, RMSSD, LF/HF ratio) [17]. ...
The current study investigated heart rate (HR) and heart rate variability (HRV) across day and night in patients with disorders of consciousness (DOC). We recorded 24-h ECG in 26 patients with DOC (i.e., unresponsive wakefulness syndrome [UWS; n=16] and (exit) minimally conscious state [(E)MCS; n=10]). To examine diurnal variations, HR and HRV indices in the time, frequency, and entropy domains were computed for periods of clear day- (forenoon: 8am-2pm; afternoon: 2pm-8pm) and nighttime (11pm-5am). Results indicate that patients' interbeat intervals (IBIs) were larger during the night than during the day indicating HR slowing. Additionally, higher HRV entropy was associated with higher EEG entropy during the night. Patients in UWS showed larger IBIs compared to patients in (E)MCS, and patients with non-traumatic brain injury showed lower ECG entropy than patients with traumatic brain injury. Thus, cardiac activity varies with a diurnal pattern in patients with DOC and can differentiate between patients' diagnoses and etiologies. Moreover, also the interaction of heart and brain appears to follow a diurnal rhythm. Thus, HR and HRV seem to mirror the integrity of brain functioning and consequently might serve as supplementary measures for improving the validity of assessments in patients with DOC.
... Complexity or coherency within the heart rhythm can be estimated from EKG RR time series. Complexity is largely driven by muscarinic cholinergic input to the SAN, while coherency results largely from sympathetic autonomic input to the SAN (Goldberger, 1991;Goldberger et al., 2002;Thayer et al., 2010). Based upon our findings that a coupled-clock system regulates the spontaneous AP firing rate of isolated SAN cells ( Lakatta et al., 2010;Yaniv et al., 2014b), we hypothesized that overexpression of AC8 within the SAN cells would generate an increased mean HR in TG AC8 mice in vivo. ...
Heart rate (HR) and HR variability (HRV), predictors of over-all organism health, are widely believed to be driven by autonomic input to the sinoatrial node (SAN), with sympathetic input increasing HR and reducing HRV. However, variability in spontaneous beating intervals in isolated SAN tissue and single SAN cells, devoid of autonomic neural input, suggests that clocks intrinsic to SAN cells may also contribute to HR and HRV in vivo. We assessed contributions of both intrinsic and autonomic neuronal input mechanisms of SAN cell function on HR and HRV via in vivo, telemetric EKG recordings. This was done in both wild type (WT) mice, and those in which adenylyl cyclase type 8 (ADCY8), a main driver of intrinsic cAMP-PKA-Ca²⁺ mediated pacemaker function, was overexpressed exclusively in the heart (TGAC8). We hypothesized that TGAC8 mice would: (1) manifest a more coherent pattern of HRV in vivo, i.e., a reduced HRV driven by mechanisms intrinsic to SAN cells, and less so to modulation by autonomic input and (2) utilize unique adaptations to limit sympathetic input to a heart with high levels of intrinsic cAMP-Ca²⁺ signaling. Increased adenylyl cyclase (AC) activity in TGAC8 SAN tissue was accompanied by a marked increase in HR and a concurrent marked reduction in HRV, both in the absence or presence of dual autonomic blockade. The marked increase in intrinsic HR and coherence of HRV in TGAC8 mice occurred in the context of: (1) reduced HR and HRV responses to β-adrenergic receptor (β-AR) stimulation; (2) increased transcription of genes and expression of proteins [β-Arrestin, G Protein-Coupled Receptor Kinase 5 (GRK5) and Clathrin Adaptor Protein (Dab2)] that desensitize β-AR signaling within SAN tissue, (3) reduced transcripts or protein levels of enzymes [dopamine beta-hydorxylase (DBH) and phenylethanolamine N-methyltransferase (PNMT)] required for catecholamine production in intrinsic cardiac adrenergic cells, and (4) substantially reduced plasma catecholamine levels. Thus, mechanisms driven by cAMP-PKA-Ca²⁺ signaling intrinsic to SAN cells underlie the marked coherence of TGAC8 mice HRV. Adaptations to limit additional activation of AC signaling, via decreased neuronal sympathetic input, are utilized to ensure the hearts survival and prevent Ca²⁺ overload.
... As the sequence of heart beats is not regular and exhibit complex fluctuation patterns over a wide range of time scales, HRV is better described by the mathematical chaos (32,33), therefore non-linear analyses are appropriate to model this type of time series. These analyses quantify the unpredictability and complexity of the interbeat intervals (IBI) series. ...
Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity.
Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality.
Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN).
Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.
... In 1988, it was reported that patients prone to high risk of sudden cardiac death showed evidence of nonlinear heart rate (HR) dynamics, including abrupt spectral changes and sustained low-frequency (LF) oscillations. After this report, it has been suggested that a loss of complex physiological variability could occur under certain pathological conditions such as reduced HR dynamics before sudden death and aging [23,24]. ...
Background:
It was developed a methodology to assess cardiac dynamic based on dynamical systems theory, probability and entropy proportions that allows the establishment of diagnostic, objective and reproducible measurements.
Objective:
To develop a diagnostic concordance study to confirm the clinical applicability of the methodology designed to assess adult cardiac dynamic, through probability and entropy proportions.
Methods:
A blind study was conducted to analyze the behavior of 550 continuous electrocardiographic recordings and Holters. For this purpose, the maximum and minimum heart rate each hour in 18 hours and the beats per hour were taken to generate a numerical attractor for each dynamic in a delay map. Subsequently, frequency, probability and ratio S/k of ordered pairs of heart rates in three regions of the attractor were calculated. After that, entropy proportions were obtained and mathematical-physical diagnosis was established to compare the results obtained through this methodology with the conventional diagnosis, taken as gold standard.
Results:
The numerical attractors for each cardiac dynamic, quantified with entropy proportions, lead to accurate mathematical distinctions between patients with normal cardiac dynamics and those with varying degrees of acute cardiac pathologies. Sensitivity and specificity were 100% and kappa coefficient had a value of 1.
Conclusion:
The diagnostic and predictive ability of the methodology was confirmed, it simplifies all current clinical parameters and allows to determine quantitatively the worsening of acute cardiac states.
Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.
The polyspecific antibody synthesis in multiple sclerosis (MS) gained diagnostic relevance with the frequent combination of measles-, rubella- and varicella zoster antibodies (MRZ-antibody reaction) but their pathophysiological role remains unknown. This review connects the data for intrathecal polyspecific antibody synthesis in MS and neurolupus with observations in the blood of patients with Guillain-Barré syndrome (GBS). Simultaneously increased antibody and autoantibody titers in GBS blood samples indicate that the polyspecific antibodies are based on a general property of an immune network, supported by the deterministic day-to-day concentration variation of antibodies in normal blood. Strongly correlated measles- and rubella- antibody variations point to a particular connectivity between the MRZ antibodies. The immune network, which provides serological memory in the absence of an antigen, implements the continuous change of the MRZ pattern in blood, not followed by the earlier immigrated B cells without corresponding connectivity in the brain. This may explain the different antibody patterns in cerebrospinal fluid, aqueous humor and blood of the individual MS patient. A complexity approach must implement a different view on causation in chronic diseases and causal therapies.
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