Complex systems and the technology of variability analysis

Fractal correlation properties of heart rate variability and respiratory frequency as measures of endurance exercise durability

PURPOSE

Field-based measures of durability (exercise-related physiologic deterioration over time) for assessing athletic fitness often rely on changes in maximal power profiles or heart rate (HR) drift. This study aimed to determine whether an index of HR variability based on the short-term exponent of Detrended Fluctuation Analysis (DFA a1) along with respiratory frequency (fB) could demonstrate changes in durability during a Time to Task Failure (TTF) Trial.

METHODS

Ten participants performed a cycling TTF at an intensity of 95% of the respiratory compensation point (RCP) on two occasions, Control and a "Reward" where a monetary incentive was offered when task failure was signaled. Metabolic responses including oxygen uptake (V ˙ O 2 ), lactate and glucose along with HR, DFA a1 and fB were measured and compared over each quarter of the TTF up to the time of signaling (Q1, Q2, Q3, and Q4).

RESULTS

The elapsed time of TTF sessions was statistically similar (p = 0.54). After initial equilibration, metabolic responses remained largely stable over Q2-Q4. HR, DFA a1 and fB displayed drift over Q2-Q4 with significant ANOVA. Repeatability of quarterly HR, DFA a1, and fB between Control and Reward sessions was high with ICC between 0.73 and 0.94, Pearson's r was between 0.83 and 0.98 with no difference in mean values by paired t testing.

CONCLUSION

HR, fB and DFA a1 are useful metrics representing alteration in physiologic characteristics demonstrating durability loss during an endurance exercise session. These measures were repeatable across sessions and have the potential to be monitored retrospectively or in real time in the field with low-cost consumer equipment.

Perinatal Psychoneuroimmunology of Prenatal Stress and Its Effects on Fetal and Postnatal Brain Development

Prenatal stress (PS) impacts early behavioral, neuroimmune, and cognitive development. Pregnant rat models have been very valuable in examining the mechanisms of such fetal programming. A pregnant sheep model of maternal stress offers the unique advantages of chronic in utero monitoring and manipulation. This chapter presents the techniques used to model single and multigenerational stress exposures and their pleiotropic effects on the offspring.

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools

BACKGROUND

Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring.

METHODS

We perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring.

RESULTS

Development and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial).

CONCLUSIONS

The proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care.

Improving the Agreement Between the First Heart-Rate-Variability Threshold and the Gas-Exchange Threshold

PURPOSE

The first heart-rate (HR) -variability (HRV) -derived threshold based on detrended fluctuation analysis alpha 1 (DFA a1) has shown inconsistent agreement with the gas-exchange threshold (GET). This study examined whether a custom method of computing the first HRV threshold (HRVT1) based on individual HRV characteristics would improve agreement.

METHODS

Fourteen participants underwent ramp incremental testing measuring gas-exchange variables and RR intervals. Comparisons were made between the oxygen consumption (V˙O2)/HR at the GET versus the V˙O2/HR at the standard DFA a1 = 0.75 (HRVT1s) or a custom value (HRVT1c) based on the DFA a1 midway between the maximum seen during the early ramp incremental and 0.5.

RESULTS

Mean values for GET V˙O2 versus HRVT1s V˙O2 and GETHR versus HRVT1sHR were statistically different (25.4 [3.3] vs 29.8 [6.8] mL·kg-1·min-1, P = .01, d = 0.80; 131 [11] vs 146 [22] beats·min-1, P = .005, d = 0.91). There were no statistical differences when using the HRVT1c (25.4 [3.3] vs 25.1 [5.7] mL·kg-1·min-1, P = .77, d = 0.08; 131 [11] vs 132 [17] beats·min-1, P = .65, d = 0.12). Equivalence between GET and HRVT1c V˙O2/HR was also verified. Mean maximal DFA a1 during the early ramp incremental was 1.52 (0.22) with mean HRVT1c of 1.01 (0.11). Pearson r correlation coefficients were between .67 and .70 for all GET to HRVT1 comparisons. The second HRV threshold and respiratory compensation point parameters showed agreement and correlations in line with prior studies.

CONCLUSIONS

The HRVT1c showed stronger agreement to GET parameters than seen using the HRVT1s. It is recommended that evaluations of the HRVT1 consider this approach in determining the HR and V˙O2 at this threshold.

Extubation Advisor: Implementation and Evaluation of A Novel Extubation Clinical Decision Support Tool

IMPORTANCE

Extubation Advisor (EA) is a novel software tool that generates a synoptic report for each Spontaneous Breathing Trial (SBT) conducted to inform extubation decision-making.

OBJECTIVES

To assess bedside EA implementation, perceptions of utility, and identify barriers and facilitators of use.

DESIGN, SETTING AND PARTICIPANTS

We conducted a phase I mixed-methods interventional study in three mixed intensive care unit (ICUs) in two academic hospitals. We interviewed critical care physicians (MDs) and respiratory therapists (RTs) regarding user-centered design principles and usability.

ANALYSIS

We evaluated our ability to consent participants (feasibility threshold 50%), capture complete data (threshold 90%), generate and review EA reports in real-time (thresholds 75% and 80%, respectively), and MD perception of tool usefulness (6-point Likert scale). We analyzed interview transcripts using inductive coding to identify facilitators and barriers to EA implementation and perceived benefit of tool use.

RESULTS

We enrolled 31 patients who underwent 70 SBTs. Although consent rates [31/31 (100%], complete data capture [68/68 (100%)], and EA report generation [68/70 (97.1%)] exceeded feasibility thresholds, reports were reviewed by MDs for [55/70 (78.6%)] SBTs. Mean MD usefulness score was 4.0/6. Based on feedback obtained from 36 interviews (15 MDs, 21 RTs), we revised the EA report twice and identified facilitators (ability to track patient progress, enhance extubation decision-making, and provide support in resource-limited settings) and barriers (resource constraints, need for education) to tool implementation. Half of respondents (9 MDs, 9 RTs; combined 50%) perceived definite or potential benefit to EA tool use.

CONCLUSION

This is the first study of a waveform-based variability-derived, predictive clinical decision support tool evaluated in adult ICUs. Our findings support the feasibility of integrating the EA tool into bedside workflow. Clinical trials are needed to assess the utility of the EA tool in practice and its impact on extubation decision-making and outcomes.

TRIAL REGISTRATION

NCT04708509.

Random forest-based prediction of intracranial hypertension in patients with traumatic brain injury

BACKGROUND

Treatment and prevention of intracranial hypertension (IH) to minimize secondary brain injury are central to the neurocritical care management of traumatic brain injury (TBI). Predicting the onset of IH in advance allows for a more aggressive prophylactic treatment. This study aimed to develop random forest (RF) models for predicting IH events in TBI patients.

METHODS

We analyzed prospectively collected data from patients admitted to the intensive care unit with invasive intracranial pressure (ICP) monitoring. Patients with persistent ICP > 22 mmHg in the early postoperative period (first 6 h) were excluded to focus on IH events that had not yet occurred. ICP-related data from the initial 6 h were used to extract linear (ICP, cerebral perfusion pressure, pressure reactivity index, and cerebrospinal fluid compensatory reserve index) and nonlinear features (complexity of ICP and cerebral perfusion pressure). IH was defined as ICP > 22 mmHg for > 5 min, and severe IH (SIH) as ICP > 22 mmHg for > 1 h during the subsequent ICP monitoring period. RF models were then developed using baseline characteristics (age, sex, and initial Glasgow Coma Scale score) along with linear and nonlinear features. Fivefold cross-validation was performed to avoid overfitting.

RESULTS

The study included 69 patients. Forty-three patients (62.3%) experienced an IH event, of whom 30 (43%) progressed to SIH. The median time to IH events was 9.83 h, and to SIH events, it was 11.22 h. The RF model showed acceptable performance in predicting IH with an area under the curve (AUC) of 0.76 and excellent performance in predicting SIH (AUC = 0.84). Cross-validation analysis confirmed the stability of the results.

CONCLUSIONS

The presented RF model can forecast subsequent IH events, particularly severe ones, in TBI patients using ICP data from the early postoperative period. It provides researchers and clinicians with a potentially predictive pathway and framework that could help triage patients requiring more intensive neurological treatment at an early stage.

Physiological network approach to prognosis in cirrhosis: A shifting paradigm

Decompensated liver disease is complicated by multi-organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.

The Clinical Role of Heart Rate Variability Assessment in Cognitively Impaired Patients and Its Applicability in Community Care Settings: A Systematic Review of the Literature

Heart rate variability (HRV) correlates well with a person's overall physiological function. Clinically, HRV is successfully used in acute care to identify impending infections, but little is known about its potential in the management of chronic diseases like cognitive decline/dementia. The aim of this study was to identify the best available knowledge about HRV in cognitively impaired populations that might be applied to improve clinical practice in community settings. We conducted a systematic literature search in PubMed, Embase, and Cochrane databases published from January 2009 to August 2022. Eligible studies were selected using Covidence and each study underwent qualitative assessment using the Mixed Method Appraisal Tool. At each stage of selection, each study was reviewed independently by two members of the team, and any disputes were discussed along the way. The literature identified that the brain regions controlling HRV are also those affected by dementias of Alzheimer's type (AD) and Lewy body types (DLB). HRV was impaired in both types, with DLB showing greater impairment in all HRV parameters compared to AD. No studies explored the temporal changes of HRV or its use in the clinical management of people with cognitive impairment (CI). The current lack of standardization of HRV recording and analysis limits its use in clinical practice. HRV may emerge as a potentially useful tool to identify people with early/preclinical memory impairment and help to differentiate AD from DLB. Longitudinal HRV measurement is emerging as a useful way to monitor disease progression and treatment response, and continuous HRV measurement may prove useful in the early identification of sepsis and its complications in patients no longer able to communicate their illness experiences.

Overnight Heart Rate Variability During Sleep Disturbance In Peri- And Postmenopausal Women

OBJECTIVES

Disturbed sleep, common during the climacteric, is associated with increased sympathetic activity, a cardiovascular risk factor. We evaluated sleep disturbance effect on autonomic nervous function in climacteric women.

METHODS

Seventeen perimenopausal and 18 postmenopausal women underwent a sleep study protocol: an adaptation night, a reference night, and a sleep disturbance night, with a hand loosely tied to the bed to allow blood sampling. This procedure was repeated after six months of menopausal hormone therapy (MHT) or placebo. Sleep disturbance and MHT effects on overnight heart rate variability (HRV) were analyzed.

RESULTS

At baseline, sleep disturbance increased vagal HRV in postmenopausal women, but no changes were seen in perimenopausal women. At six months, sleep disturbance increased total HRV power in the perimenopausal placebo group, and increased nonlinear vagal HRV in the postmenopausal placebo group, but no other changes were seen. MHT did not have any effects on HRV, neither at perimenopause nor at postmenopause.

CONCLUSIONS

External sleep disturbance had only minor effects on HRV across menopause. MHT had no detectable HRV effects.

Ventilator Weaning and Extubation

Increasing evidence supports specific approaches to liberate patients from invasive ventilation including the use of liberation protocols, inspiratory assistance during spontaneous breathing trials (SBTs), early extubation of patients with chronic obstructive pulmonary disease to noninvasive ventilation, and prophylactic use of noninvasive support strategies after extubation. Additional research is needed to elucidate the best criteria to identify patients who are ready to undergo an SBT and to inform optimal screening frequency, the best SBT technique and duration, extubation assessments, and extubation decision-making. Additional clarity is also needed regarding the optimal timing to measure and report extubation success.

Heart rate dynamics and asymmetry during sympathetic activity stimulation and post-stimulation recovery in ski mountaineers-a pilot exploratory study

There is a lack of studies on non-linear heart rate (HR) variability in athletes. We aimed to assess the usefulness of short-term HR dynamics and asymmetry parameters to evaluate the neural modulation of cardiac activity based on non-stationary RR interval series by studying their changes during sympathetic nervous system activity stimulation (isometric handgrip test) and post-stimulation recovery in professional ski mountaineers. The correlation between the changes in the parameters and the respiratory rate (RespRate) and also the duration of the career was analyzed. Short-term (5 min) and ultra-short-term (1 min) rates of patterns with no variations (0V), number of acceleration runs of length 1 (AR1), and short-term Porta's Index were greater, whereas Guzik's Index (GI) was smaller during sympathetic stimulation compared to rest. GI increased and the number of AR1 decreased during recovery. Greater increases in GI and RMSSD were associated with greater decreases in RespRate during recovery. Greater increases in RespRate from rest to short-term sympathetic stimulation were associated with greater increases in 0V (Max-min method) and AR1 but also with greater decreases in decelerations of short-term variance and accelerations and decelerations of long-term variance. Greater increases in 0V (Max-min method) and number of AR1 during sympathetic stimulation were associated with a shorter career duration. Greater decreases in these parameters during recovery were associated with a longer career duration. Changes in measures of HR dynamics and asymmetry, calculated based on short-term non-stationary RRi time series induced by sympathetic stimulation and post-stimulation recovery, reflected sympathovagal shift and were associated with condition-related alterations in RespRate and career duration in athletes who practice ski mountaineering.

The effects of acute exercise and a nap on heart rate variability and memory in young sedentary adults

Recent evidence suggests that the autonomic nervous system can contribute to memory consolidation during sleep. Whether fluctuations in cardiac autonomic activity during sleep following physical exercise contribute to the process of memory consolidation has not been studied. We assessed the effects of a non-rapid eye movement (NREM) nap following acute exercise on cardiac autonomic regulation assessed with heart rate variability (HRV) to examine if HRV influences memory processes. Fifty-six (59% female) healthy young adults (23.14 ± 3.74 years) were randomly allocated to either the exercise plus nap (ExNap, n = 27) or nap alone (NoExNap, n = 29) groups. The ExNap group performed a 40-minute moderate-intensity cycling, while the NoExNap group was sedentary prior to learning 45 neutral pictures for a later test. Subsequently, participants underwent a 60-minute NREM nap while measuring EKG, followed by a visual recognition test. Our results indicated that heart rate did not significantly differ between the groups (p = .243), whereas vagally mediated HRV indices were lower in the ExNap group compared to the NoExNap group (p < .05). There were no significant differences in sleep variables between the groups (p > .05). Recognition accuracy was significantly higher in the ExNap group than in the NoExNap group (p = .027). In addition, the recognition accuracy of the ExNap group was negatively associated with vagally mediated HRV (p < .05). Pre-nap acute exercise appears to attenuate parasympathetic activity and to alter the relationship between memory and cardiac autonomic activity.

Combining Near-Infrared Spectroscopy and Heart Rate Variability Derived Thresholds to Estimate the Critical Intensity of Exercise

ABSTRACT

Fleitas-Paniagua, PR, de Almeida Azevedo, R, Trpcic, M, Murias, JM, and Rogers, B. Combining near-infrared spectroscopy and heart rate variability derived thresholds to estimate the critical intensity of exercise. J Strength Cond Res 38(1): e16-e24, 2024-Critical intensity determination often requires costly tools and several testing sessions. Alternative approaches display relatively large individual variation. Therefore, simpler estimations with improved precision are needed. This study evaluated whether averaging the heart rate (HR) and oxygen uptake (V̇O 2 ) responses associated with the muscle deoxyhemoglobin concentration breakpoint ([HHb] BP ) and the heart rate variability (HRV) given by the detrended fluctuation analysis second threshold (HRVT2) during ramp incremental (RI) test improved the accuracy of identifying the HR and V̇O 2 at the respiratory compensation point (RCP). Ten female and 11 male recreationally trained subjects performed a 15 W·minute -1 RI test. Gas exchange, near-infrared spectroscopy (NIRS), and RR interval were recorded to assess the RCP, [HHb] BP , and HRVT2. Heart rate (mean ± SD : 158 ± 14, 156 ± 13, 160 ± 14 and, 158 ± 12 bpm) and V̇O 2 (3.08 ± 0.69, 2.98 ± 0.58, 3.06 ± 0.65, and 3.02 ± 0.60 L·minute -1 ) at the RCP, [HHb] BP , HRVT2, and HRVT2&[HHb] BP average (H&H Av ), respectively, were not significantly different ( p > 0.05). The linear relationship between H&H Av and RCP was higher compared with the relationship between [HHb] BP vs RCP and HRVT2 vs RCP for both HR ( r = 0.85; r = 0.73; r = 0.79, p > 0.05) and V̇O 2 ( r = 0.94; r = 0.93; r = 0.91, p > 0.05). Intraclass correlation between RCP, [HHb] BP , HRVT2, and H&H AV was 0.93 for V̇O 2 and 0.79 for HR. The [HHb] BP and the HRVT2 independently provided V̇O 2 and HR responses that strongly agreed with those at the RCP. Combining [HHb] BP and the HRVT2 resulted in estimations of the V̇O 2 and HR at the RCP that displayed smaller variability compared with each modality alone.

Pupillary dynamics predict long-term outcome in a cohort of acute traumatic brain injury coma patients

OBJECTIVE

Examining the size and reactivity of the pupils of traumatic brain injury coma patients is fundamental in the Neuro-intensive care unit (ICU). Pupil parameters on admission predict long-term clinical outcomes. However, little is known about the dynamics of pupillary parameters and their potential value for outcome prediction.

METHODS

This study applied a time-course analysis of pupillary signals (size and photo-reactivity) in acute traumatic brain injury coma patients (n = 20) to predict outcome at 6 months.

RESULTS

The time course of pupillary signals was informative in discriminating favorable (F) versus unfavorable (U) outcomes, with the highest correlation within the 1st week notwithstanding pharmacological sedation. Patients with favorable outcome at 6 months showed more consistent in time isochoric and photo-reactive pupils. In contrast, patients with an unfavorable outcome showed more variable measures that tended to stabilize toward pathological values.

INTERPRETATION

Time-dependent tracking of pupils' size and reactivity is a promising application for ICU monitoring and long-term prognosis. These findings support the usefulness of automatic tools for the dynamic, quantitative, and objective measurements of pupils.

Assessment and Therapeutic Modulation of Heart Rate Variability: Potential Implications in Patients with COVID-19

Cardiac damage has been attributed to SARS-CoV-2-related pathology contributing to increased risk of vascular events. Heart rate variability (HRV) is a parameter of functional neurocardiac integrity with low HRV constituting an independent predictor of cardiovascular mortality. Whether structural cardiac damage translates into neurocardiac dysfunction in patients infected with SARS-CoV-2 remains poorly understood. Hypothesized mechanisms of possible neurocardiac dysfunction in COVID-19 comprise direct systemic neuroinvasion of autonomic control centers, ascending virus propagation along cranial nerves and cardiac autonomic neuropathy. While the relationship between the autonomic nervous system and the cytokine cascade in general has been studied extensively, the interplay between the inflammatory response caused by SARS-CoV-2 and autonomic cardiovascular regulation remains largely unclear. We reviewed the current literature on the potential diagnostic and prognostic value of autonomic neurocardiac function assessment via analysis of HRV including time domain and spectral analysis techniques in patients with COVID-19. Furthermore, we discuss potential therapeutic targets of modulating neurocardiac function in this high-risk population including HRV biofeedback and the impact of long COVID on HRV as well as the approaches of clinical management. These topics might be of particular interest with respect to multimodal pandemic preparedness concepts.

Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

BACKGROUND

Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients.

METHODS

Patients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups.

RESULTS

In total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time.

CONCLUSIONS

We showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time. Trial registration The protocol of this study is published by Quinten et al., 2017.

Measuring complexity of muscle force control: Theoretical principles and clinical relevance in musculoskeletal research and practice

Musculoskeletal conditions affect bones, joints, and muscles of the locomotor system and are a leading cause of disability worldwide. This suggests that current musculoskeletal rehabilitation techniques fail to target the characteristics (e.g., physiological/physical/psychological) most influential for long-term musculoskeletal health. To identify whether a physiological characteristic is impaired, it must be measured. In neuromuscular control, traditional research approaches use magnitude-based measurements (e.g., peak force/standard deviation of force/coefficient of variation of force). However, magnitude-based measurements miss 'hidden information' regarding a physiological system's status across time. To better identify physiological characteristics that are clinically-important for long-term musculoskeletal health, other measurement approaches currently less applied in musculoskeletal research may be helpful. The purpose of this article is to present an introduction to technical and measurement principles for quantifying the 'complexity' of muscle force control as one representation of peripheral joint neuromuscular control. Complexity measurements are time-based and consider the irregular temporal structure of physiological signals. We review theoretical principles underlying measuring complexity of muscle force control and explain its clinical relevance for musculoskeletal scientists and clinicians. The principles include sensorimotor control of peripheral joints, muscle force signal construction and features, muscle force control measurement procedures, and variability and complexity variables. We propose the potential utility of measuring the complexity of muscle force control for diagnosing sensorimotor system impairment and prognosis following musculoskeletal disease or injury. This article will serve as an educational asset and a scientific resource that will inform future research directions to optimise rehabilitation for people with peripheral joint disease and injury.

Comprehensive breathing variability indices enhance the prediction of extubation failure in patients on mechanical ventilation

BACKGROUND AND OBJECTIVE

Despite the numerous studies on extubation readiness assessment for patients who are invasively ventilated in the intensive care unit, a 10-15% extubation failure rate persists. Although breathing variability has been proposed as a potential predictor of extubation failure, it is mainly assessed using simple statistical metrics applied to basic respiratory parameters. Therefore, the complex pattern of breathing variability conveyed by continuous ventilation waveforms may be underexplored.

METHODS

Here, we aimed to develop novel breathing variability indices to predict extubation failure among invasively ventilated patients. First, breath-to-breath basic and comprehensive respiratory parameters were computed from continuous ventilation waveforms 1 h before extubation. Subsequently, the basic and advanced variability methods were applied to the respiratory parameter sequences to derive comprehensive breathing variability indices, and their role in predicting extubation failure was assessed. Finally, after reducing the feature dimensionality using the forward search method, the combined effect of the indices was evaluated by inputting them into the machine learning models, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost).

RESULTS

The coefficient of variation of the dynamic mechanical power per breath (CV-MPd[J/breath]) exhibited the highest area under the receiver operating characteristic curve (AUC) of 0.777 among the individual indices. Furthermore, the XGBoost model obtained the best AUC (0.902) by combining multiple selected variability indices.

CONCLUSIONS

These results suggest that the proposed novel breathing variability indices can improve extubation failure prediction in invasively ventilated patients.

Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes

Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.

An Exploratory Study of Sleep-Wake Differences of Autonomic Activity in Patients with Mild Cognitive Impairment: The Role of Melatonin as a Modulating Factor

Purpose

The objective of the present study was to assess sleep-wake differences of autonomic activity in patients with mild cognitive impairment (MCI) compared to control subjects. As a post-hoc objective, we sought to evaluate the mediating effect of melatonin on this association.

Patients and Methods

A total of 22 MCI patients (13 under melatonin treatment) and 12 control subjects were included in this study. Sleep-wake periods were identified by actigraphy and 24hr-heart rate variability measures were obtained to study sleep-wake autonomic activity.

Results

MCI patients did not show any significant differences in sleep-wake autonomic activity when compared to control subjects. Post-hoc analyses revealed that MCI patients not taking melatonin displayed lower parasympathetic sleep-wake amplitude than controls not taking melatonin (RMSSD -7 ± 1 vs 4 ± 4, p = 0.004). In addition, we observed that melatonin treatment was associated with greater parasympathetic activity during sleep (VLF 15.5 ± 0.1 vs 15.1 ± 0.1, p = 0.010) and in sleep-wake differences in MCI patients (VLF 0.5 ± 0.1 vs 0.2 ± 0.0, p = 0.004).

Conclusion

These preliminary findings hint at a possible sleep-related parasympathetic vulnerability in patients at prodromal stages of dementia as well as a potential protective effect of exogenous melatonin in this population.

Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and Training Prescription in Endurance Exercise: An Update

While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.

Patterned Illumination Techniques in Optogenetics: An Insight Into Decelerating Murine Hearts

Much has been reported about optogenetic based cardiac arrhythmia treatment and the corresponding characterization of photostimulation parameters, but still, our capacity to interact with the underlying spatiotemporal excitation patterns relies mainly on electrical and/or pharmacological approaches. However, these well-established treatments have always been an object of somehow heated discussions. Though being acutely life-saving, they often come with potential side-effects leading to a decreased functionality of the complex cardiac system. Recent optogenetic studies showed the feasibility of the usage of photostimulation as a defibrillation method with comparatively high success rates. Although, these studies mainly concentrated on the description as well as on the comparison of single photodefibrillation approaches, such as locally focused light application and global illumination, less effort was spent on the description of excitation patterns during actual photostimulation. In this study, the authors implemented a multi-site photodefibrillation technique in combination with Multi-Lead electrocardiograms (ECGs). The technical connection of real-time heart rhythm measurements and the arrhythmia counteracting light control provides a further step toward automated arrhythmia classification, which can lead to adaptive photodefibrillation methods. In order to show the power effectiveness of the new approach, transgenic murine hearts expressing channelrhodopsin-2 ex vivo were investigated using circumferential micro-LED and ECG arrays. Thus, combining the best of two methods by giving the possibility to illuminate either locally or globally with differing pulse parameters. The optical technique presented here addresses a number of challenges of technical cardiac optogenetics and is discussed in the context of arrhythmic development during photostimulation.

Characterizing the effect of incrementally increasing dry bulb temperature on linear and nonlinear measures of heart rate variability in nonpregnant, mid-gestation, and late-gestation sows

Characterizing the sow physiological response to an increased heat load is essential for effective heat stress mitigation. The study objective was to characterize the effects of a 400-min heating episode on sow heart rate variability (HRV) at different reproductive stages. HRV is a commonly used noninvasive proxy measure of autonomic function. Twenty-seven sows were enrolled in the study according to their gestation stage at time of selection: 1) nonpregnant (NP; n = 7), 2) mid-gestation (MID; 57.3 ± 11.8 d gestation; n = 11), and 3) late-gestation (LATE; 98.8 ± 4.9 d gestation; n = 8). The HRV data utilized in the study were collected from each pig as the dry bulb temperature in the room increased incrementally from 19.84 ± 2.15 °C to 35.54 ± 0.43 °C (range: 17.1-37.5 °C) over a 400-min period. After data collection, one 5-min set of continuous heart rate data were identified per pig for each of nine temperature intervals (19-20.99, 21-22.99, 23-24.99, 25-26.99, 27-28.99, 29-30.99, 31-32.99, 33-34.99, and 35-36.99 °C). Mean inter-beat interval length (RR), standard deviation of r-r intervals (SDNN), root mean square of successive differences (RMSSD), high frequency spectral power (HF), sample entropy (SampEn), short-term detrended fluctuation analysis (DFAα1), and three measures (%REC, DET, LMEAN) derived from recurrence quantification analysis were calculated for each data set. All data were analyzed using the PROC GLIMMIX procedure in SAS 9.4. Overall, LATE sows exhibited lower RR than NP sows (P < 0.01). The standard deviation of r-r intervals and RMSSD differed between each group (P < 0.01), with LATE sows exhibiting the lowest SDNN and RMSSD and NP sows exhibiting the greatest SDNN and RMSSD. Late-gestation sows exhibited lower HF than both MID and NP sows (P < 0.0001), greater DFA values than NP sows (P = 0.05), and greater DET compared to MID sows (P = 0.001). Late-gestation also sows exhibited greater %REC and LMEAN compared to MID (P < 0.01) and NP sows (all P < 0.01). In conclusion, LATE sows exhibited indicators of greater autonomic stress throughout the heating period compared to MID and NP sows. However, temperature by treatment interactions were not detected as dry bulb increased. Future studies are needed to fully elucidate the effect of gestational stage and increasing dry bulb temperature on sow HRV.

Exploring the Dynamics of Week-to-Week Blood Pressure in Nursing Home Residents Before Death

BACKGROUND

Aging is accompanied by an overall dysregulation of many dynamic physiologic processes including those related to blood pressure (BP). While year-to-year BP variability is associated with cardiovascular events and mortality, no studies have examined this trend with more frequent BP assessments. Our study objective is to take the next step to examine week-to-week BP dynamics-pattern, variability, and complexity-before death.

METHODS

Using a retrospective study design, we assessed BP dynamics in the 6 months before death in long-term nursing home residents between 1 October 2006 and 30 September 2017. Variability was characterized using SD and mean squared error after adjusting for diurnal variations. Complexity (i.e., amount of novel information in a trend) was examined using Shannon's entropy (bits). Generalized linear models were used to examine factors associated with overall BP variability.

RESULTS

We identified 17,953 nursing home residents (98.0% male, 82.5% White, mean age 80.2 years, and mean BP 125.7/68.6 mm Hg). Despite a slight trend of decreasing systolic week-to-week BP over time (delta = 7.2 mm Hg), week-to-week complexity did not change in the 6 months before death (delta = 0.02 bits). Average weekly BP variability was stable until the last 3-4 weeks of life, at which point variability increased by 30% for both systolic and diastolic BP. Factors associated with BP variability include average weekly systolic/diastolic BP, days in the nursing home, days in the hospital, and changes to antihypertensive medications.

CONCLUSIONS

Week-to-week BP variability increases substantially in the last month of life, but complexity does not change. Changes in care patterns may drive the increase in BP variability as one approaches death.

Differentiating acute from chronic insomnia with machine learning from actigraphy time series data

Acute and chronic insomnia have different causes and may require different treatments. They are investigated with multi-night nocturnal actigraphy data from two sleep studies. Two different wrist-worn actigraphy devices were used to measure physical activities. This required data pre-processing and transformations to smooth the differences between devices. Statistical, power spectrum, fractal and entropy analyses were used to derive features from the actigraphy data. Sleep parameters were also extracted from the signals. The features were then submitted to four machine learning algorithms. The best performing model was able to distinguish acute from chronic insomnia with an accuracy of 81%. The algorithms were then used to evaluate the acute and chronic groups compared to healthy sleepers. The differences between acute insomnia and healthy sleep were more prominent than between chronic insomnia and healthy sleep. This may be associated with the adaptation of the physiology to prolonged periods of disturbed sleep for individuals with chronic insomnia. The new model is a powerful addition to our suite of machine learning models aiming to pre-screen insomnia at home with wearable devices.

The effect of passive lower limb training on heart rate asymmetry

OBJECTIVE

Heart rate asymmetry (HRA) is an approach for quantitatively assessing the uneven distribution of heart rate accelerations and decelerations for sinus rhythm. We aimed to investigate whether automatic regulation led to HRA alternation during passive lower limb training.

METHODS

Thirty healthy participants were recruited in this study. The protocol included a baseline (Pre-E) and three passive lower limb training trials (E1, E2 and E3) with a randomized order. Several variance-based HRA variables were established. Heart rate variability (HRV) parameters, i.e., mean RR, SDNN, RMSSD, LF (n.u.), HF (n.u.) and VLF (ms2), and HRA variables, i.e., SD1a, SD1d, SD2a, SD2d, SDNNa and SDNNd, were calculated by using 5-min RR time series, as well as the normalized HRA variables, i.e., C1a, C1d, C2a, C2d, Ca and Cd.

RESULTS

Our results showed that the performance of HRA was distinguished. The normalized HRA was observed with significant changes in E1, E2 and E3 compared to Pre -E. Moreover, parts of non-normalized HRA variables correlated with HRV parameters, which indicated that HRA might benefit in assessing cardiovascular modulation in passive lower limb training.

CONCLUSIONS

In summary, this study suggested that passive training led to significant HRA alternation and the application of HRA gave us the possibility for autonomic assessment.

EEG-based human emotion recognition using entropy as a feature extraction measure

Many studies on brain-computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.

Physiological complexity: influence of ageing, disease and neuromuscular fatigue on muscle force and torque fluctuations

NEW FINDINGS

What is the topic of this review? Physiological complexity in muscle force and torque fluctuations, specifically the quantification of complexity, how neuromuscular complexityis altered by perturbations and the potential mechanism underlying changes in neuromuscular complexity. What advances does it highlight? The necessity to calculate both magnitude- and complexity-based measures for the thorough evaluation of force/torque fluctuations. Also the need for further research on neuromuscular complexity, particularly how it relates to the performance of functional activities (e.g. manual dexterity, balance, locomotion).

ABSTRACT

Physiological time series produce inherently complex fluctuations. In the last 30 years, methods have been developed to characterise these fluctuations, and have revealed that they contain information about the function of the system producing them. Two broad classes of metrics are used: (1) those which quantify the regularity of the signal (e.g. entropy metrics); and (2) those which quantify the fractal properties of the signal (e.g. detrended fluctuation analysis). Using these techniques, it has been demonstrated that ageing results in a loss of complexity in the time series of a multitude of signals, including heart rate, respiration, gait and, crucially, muscle force or torque output. This suggests that as the body ages, physiological systems become less adaptable (i.e. the systems' ability to respond rapidly to a changing external environment is diminished). More recently, it has been shown that neuromuscular fatigue causes a substantial loss of muscle torque complexity, a process that can be observed in a few minutes, rather than the decades it requires for the same system to degrade with ageing. The loss of torque complexity with neuromuscular fatigue appears to occur exclusively above the critical torque (at least for tasks lasting up to 30 min). The loss of torque complexity can be exacerbated with previous exercise of the same limb, and reduced by the administration of caffeine, suggesting both peripheral and central mechanisms contribute to this loss. The mechanisms underpinning the loss of complexity are not known but may be related to altered motor unit behaviour as the muscle fatigues.

Effects of yoga and mindfulness practices on the autonomous nervous system in primary school children: A non-randomised controlled study

OBJECTIVES

The present study examined the effects of a yoga and mindfulness-based programme on the autonomic nervous system of primary school children by using heart rate variability parameters.

DESIGN

A two-arm non-randomised controlled trial compared an integrated yoga and mindfulness-based programme (16 weeks) to conventional primary school lessons.

SETTING

Primary school classrooms and conference rooms.

INTERVENTIONS

Participants were allocated to a 16-week integrated yoga-based programme or conventional school lessons. A subgroup was randomised to receive 24h electrocardiogram-recordings.

MAIN OUTCOME MEASURES

Heart rate variability indices were measured, both linear (time and frequency domain) and non-linear (symbolic dynamics, compression entropy), calculated from 30-minute extracts of Holter-electrocardiogram-recordings. Assessments were conducted at baseline and at the end of intervention.

RESULTS

40 participants (42.5% female) were included into the analysis of HRV. No significant changes in heart rate variability parameters were observed between the groups after 16 weeks. In the intervention group, a trend towards increased parasympathetic activity could be seen over time, although not significantly enhanced compared to the control group.

CONCLUSION

Results obtained here do not clearly show that children in German primary school settings benefit from an integrated yoga-based intervention. However, exploratory post-hoc analyses point interestingly to an increased nocturnal parasympathetic activity in the intervention group. Further studies are required with high-quality study designs, larger sample sizes and longer-term follow-ups.

Breathing variability-implications for anaesthesiology and intensive care

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.

Prediction of mortality among patients with chronic kidney disease: A systematic review

BACKGROUND

Chronic kidney disease (CKD) is a common medical condition that is increasing in prevalence. Existing published evidence has revealed through regression analyses that several clinical characteristics are associated with mortality in CKD patients. However, the predictive accuracies of these risk factors for mortality have not been clearly demonstrated.

AIM

To demonstrate the accuracy of mortality predictive factors in CKD patients by utilizing the area under the receiver operating characteristic (ROC) curve (AUC) analysis.

METHODS

We searched Ovid MEDLINE, EMBASE, and the Cochrane Library for eligible articles through January 2021. Studies were included based on the following criteria: (1) Study nature was observational or conference abstract; (2) Study populations involved patients with non-transplant CKD at any CKD stage severity; and (3) Predictive factors for mortality were presented with AUC analysis and its associated 95% confidence interval (CI). AUC of 0.70-0.79 is considered acceptable, 0.80-0.89 is considered excellent, and more than 0.90 is considered outstanding.

RESULTS

Of 1759 citations, a total of 18 studies (n = 14579) were included in this systematic review. Eight hundred thirty two patients had non-dialysis CKD, and 13747 patients had dialysis-dependent CKD (2160 patients on hemodialysis, 370 patients on peritoneal dialysis, and 11217 patients on non-differentiated dialysis modality). Of 24 mortality predictive factors, none were deemed outstanding for mortality prediction. A total of seven predictive factors [N-terminal pro-brain natriuretic peptide (NT-proBNP), BNP, soluble urokinase plasminogen activator receptor (suPAR), augmentation index, left atrial reservoir strain, C-reactive protein, and systolic pulmonary artery pressure] were identified as excellent. Seventeen predictive factors were in the acceptable range, which we classified into the following subgroups: predictors for the non-dialysis population, echocardiographic factors, comorbidities, and miscellaneous.

CONCLUSION

Several factors were found to predict mortality in CKD patients. Echocardiography is an important tool for mortality prognostication in CKD patients by evaluating left atrial reservoir strain, systolic pulmonary artery pressure, diastolic function, and left ventricular mass index.

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.

Atypical neural processing during the execution of complex sensorimotor behavior in autism

Stereotyped behavior is rhythmic, repetitive movement that is essentially invariant in form. Stereotypy is common in several clinical disorders, such as autism spectrum disorders (ASD), where it is considered maladaptive. However, it also occurs early in typical development (TD) where it is hypothesized to serve as the foundation on which complex, adaptive motor behavior develops. This transition from stereotyped to complex movement in TD is thought to be supported by sensorimotor integration. Stereotypy in clinical disorders may persist due to deficits in sensorimotor integration. The present study assessed whether differences in sensorimotor processing may limit the expression of complex motor behavior in individuals with ASD and contribute to the clinical stereotypy observed in this population. Adult participants with ASD and TD performed a computer-based stimulus-tracking task in the presence and absence of visual feedback. Electroencephalography was recorded during the task. Groups were compared on motor performance (root mean square error), motor complexity (sample entropy), and neural complexity (multiscale sample entropy of the electroencephalography signal) in the presence and absence of visual feedback. No group differences were found for motor performance or motor complexity. The ASD group demonstrated greater neural complexity and greater differences between feedback conditions than TD individuals, specifically in signals relevant to sensorimotor processing. Motor performance and motor complexity correlated with clinical stereotypy in the ASD group. These findings support the hypothesis that individuals with ASD have differences in sensorimotor processing when executing complex motor behavior and that stereotypy is associated with low motor complexity.

Decreased sample entropy during sleep-to-wake transition in sleep apnea patients

Objective. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea.Approach. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons.Main results. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones (P < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 (P < 0.05), S2 (P < 0.01) or S4 (P < 0.05).Significance. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.

Early Warning of Infection in Patients Undergoing Hematopoietic Stem Cell Transplantation Using Heart Rate Variability and Serum Biomarkers

Early warning of infection is critical to reduce the risk of deterioration and mortality, especially in neutropenic patients following hematopoietic stem cell transplantation (HCT). Given that heart rate variability (HRV) is a sensitive and early marker for infection, and that serum inflammatory biomarkers can have high specificity for infection, we hypothesized their combination may be useful for accurate early warning of infection. In this study, we developed and evaluated a composite predictive model using continuous HRV with daily serum biomarker measurements to provide risk stratification of future deterioration in HCT recipients. A total of 116 ambulatory outpatients about to undergo HCT consented to collection of prospective demographic, clinical (daily vital signs), HRV (continuous electrocardiography [ECG] monitoring, laboratory [daily serum samples frozen at -80 °C]), and infection outcome variables (defined as the time of escalation of antibiotics), all from 24 hours pre-HCT to the onset of infection or 14 days post-HCT. Indications for antibiotic escalation were adjudicated as "true infection" or not by 2 blinded HCT clinicians. A composite time series of 8 HRV metrics was created for each patient, and the probability of deterioration within the next 72 hours was estimated using logistic regression modeling of composite HRV and serum biomarkers using a rule-based naïve Bayes model if the HRV-based probability exceeded a median threshold. Thirty-five patients (30%) withdrew within <24 hours owing to intolerability of ECG monitoring, leaving 81 patients, of whom 48 (59%) had antibiotic escalation adjudicated as true infection. The combined HRV and biomarker (TNF-α, IL-6, and IL-7) predictive model began increasing at ∼48 hours on average before the diagnosis of infection, could distinguish between high risk of impending infection (>90% incidence of subsequent infection within 72 hours), average risk (∼50%), and low risk (<10%), with an area under the receiver operating characteristic curve of 0.87. However, given that prophylactic predictive ECG monitoring and daily serum collection proved challenging for many patients, further refinement in measurement is necessary for further study.

Detection of the Anaerobic Threshold in Endurance Sports: Validation of a New Method Using Correlation Properties of Heart Rate Variability

Past attempts to define an anaerobic threshold (AnT) have relied upon gas exchange kinetics, lactate testing and field-based evaluations. DFA a1, an index of heart rate (HR) variability (HRV) fractal correlation properties, has been shown to decrease with exercise intensity. The intent of this study is to investigate whether the AnT derived from gas exchange is associated with the transition from a correlated to uncorrelated random HRV pattern signified by a DFA a1 value of 0.5. HRV and gas exchange data were obtained from 15 participants during an incremental treadmill run. Comparison of the HR reached at the second ventilatory threshold (VT2) was made to the HR reached at a DFA a1 value of 0.5 (HRVT2). Based on Bland-Altman analysis and linear regression, there was strong agreement between VT2 and HRVT2 measured by HR (r = 0.78, p < 0.001). Mean VT2 was reached at a HR of 174 (±12) bpm compared to mean HRVT2 at a HR of 171 (±16) bpm. In summary, the HR associated with a DFA a1 value of 0.5 on an incremental treadmill ramp was closely related to that of the HR at the VT2 derived from gas exchange analysis. A distinct numerical value of DFA a1 representing an uncorrelated, random interbeat pattern appears to be associated with the VT2 and shows potential as a noninvasive marker for training intensity distribution and performance status.

Low Heart Rate Variability Predicts Stroke and Other Complications in the First Six Postoperative Months After a Hip Fracture Operation

Background: One-year mortality after hip fractures is underestimated and is reported as 25%. An improved risk stratifying could contribute to a better follow up of these patients. Heart Rate Variability (HRV) is an easy point-of-care investigation and is been used in cardiology, endocrinology, and perioperative care. This observational study intended to explore relevant associations between HRV parameters and 6-months mortality and morbidity after a hip fracture. Methods: One hundred and sixty-five patients admitted to two hospitals were included, and short-time HRV measurements (5 min, and 10 min at the two hospitals, respectively) were obtained. Mortality data were gathered by means of the Norwegian central address register. Patients, close relatives of patients, and in some cases their general physicians or nursery home physicians were interviewed 6 months postoperatively regarding the incidence of pneumonia, cardiac events, or stroke. Results: One and hundred fifty-seven (95.2%) patients were followed up after 6 months post-surgery. Twenty-one (13%) died during this period. Twenty patients (13%) developed pneumonia, eight (5 %) stroke, and four (2%) myocardial infarction. No HRV parameter was associated with 6-month general mortality. However, patients who developed stroke had significantly lower High Frequency Power (HF, p < 0.001) and lower Very Low Frequency Power (VLF, p = 0.003) at inclusion compared to patients without complications. Patients who developed pneumonia had at the inclusion lower root mean square of successive differences (RMSSD, p = 0.044). Patients with a history of coronary heart disease (n = 41) showed a mortality of 7%. Mortality in this group was associated with standard deviation of beat-to-beat intervals (SDNN, p = 0.006), Total Power (TP, p = 0.009), HF (p = 0.026), and Low Frequency Power (LF, p = 0.012). Beta-blocker intake was associated with lower heart rate, but not with differences in HRV parameters. Conclusion: In this exploratory study, we present for the first-time significant associations between different preoperative HRV parameters and stroke, myocardial infarction, and pneumonia during a 6-month period after hip fracture. HRV might be a simple and effective tool to identify patients at risk that would warrant better follow-up.

Delayed association of acute particulate matter 2.5 air pollution exposure with loss of complexity in cardiac rhythm dynamics: insight from detrended fluctuation analysis

There is a delayed (lag 1 to 2 days) correlation between acute PM 2.5 (particulate matter < 2.5 μm in aerodynamic diameter) exposure and cardiovascular events, but the underlying mechanism remained unclear. We aimed to investigate the delayed impact of acute PM 2.5 exposures on cardiac autonomics through linear and nonlinear heart rate variability (HRV) analyses. Among 6912 patients who had received 24-h Holter ECG between October 1, 2015, to October 31, 2016, 56 patients (31 males, 70.3 ± 12.7 years old) were enrolled. We classified the patients as high (> 35.4 μg/m³) or low (< 35.4 μg/m³) PM 2.5 groups according to their PM 2.5 exposures on the day of Holter recordings (day 0) lag 1 and lag 2 days. Linear and nonlinear HRV parameters〔Detrended fluctuation analysis (DFA) slopes 1 and 2〕were compared. Baseline characteristics were similar between groups. Linear and nonlinear HRV parameters were similar between high- and low-exposure groups on day 0 and lag 1 day, respectively. However, DFA slope 1 was significantly lower in the high-exposure group on lag 2 days (0.784 ± 0.201 vs. 0.964 ± 0.274, p = 0.021). DFA slope 1 of the high-exposure group was significantly lower on daytime periods (9 am to 9 pm, 8 am to 4 pm and 4 pm to 12 pm) but not on nighttime periods. High lag 2 days PM 2.5 exposure is associated with low DFA slope 1 and the relationship is diurnal. This suggests that air pollution might have a delayed impact on the cardiovascular autonomic system.

A non-linear analysis of running in the heavy and severe intensity domains

PURPOSE

Altered movement complexity, indicative of system dysfunction, has been demonstrated with increased running velocity and neuromuscular fatigue. The critical velocity (CV) denotes a metabolic and neuromuscular fatigue threshold. It remains unclear whether changes to complexity during running are coupled with the exercise intensity domain in which it is performed. The purpose of this study was to examine whether movement variability and complexity differ exclusively above the CV intensity during running.

METHODS

Ten endurance-trained participants ran at 95%, 100%, 105% and 115% CV for 20 min or to task failure, whichever occurred first. Movement at the hip, knee, and ankle were sampled throughout using 3D motion analysis. Complexity of kinematics in the first and last 30 s were quantified using sample entropy (SampEn) and detrended fluctuation analysis (DFA-α). Variability was determined using standard deviation (SD).

RESULTS

SampEn decreased during all trials in knee flexion/extension and it increased in hip internal/external rotation, whilst DFA-α increased in knee internal/external rotation. SD of ankle plantar/dorsiflexion and inversion/eversion, knee internal/external rotation, and hip flexion/extension and abduction/adduction increased during trials. Hip flexion/extension SampEn values were lowest below CV. DFA-α was lower at higher velocities compared to velocities below CV in ankle plantar/dorsiflexion, hip flexion/extension, hip adduction/abduction, hip internal/external rotation. In hip flexion/extension SD was highest at 115% CV.

CONCLUSIONS

Changes to kinematic complexity over time are consistent between heavy and severe intensity domains. The findings suggest running above CV results in increased movement complexity and variability, particularly at the hip, during treadmill running.

Complexity Measures of Heart-Rate Variability in Amyotrophic Lateral Sclerosis with Alternative Pulmonary Capacities

OBJECTIVE

the complexity of heart-rate variability (HRV) in amyotrophic lateral sclerosis (ALS) patients with different pulmonary capacities was evaluated.

METHODS

We set these according to their pulmonary capacity, and specifically forced vital capacity (FVC). We split the groups according to FVC (FVC > 50% (n = 29) and FVC < 50% (n = 28)). In ALS, the presence of an FVC below 50% is indicative of noninvasive ventilation with two pressure levels and with the absence of other respiratory symptoms. As the number of subjects per group was different, we applied the unbalanced one-way analysis of variance (uANOVA1) test after three tests of normality, and effect size by Cohen's d to assess parameter significance.

RESULTS

with regard to chaotic global analysis, CFP4 (p < 0.001; d = 0.91), CFP5 (p = 0.0022; d = 0.85), and CFP6 (p = 0.0009; d = 0.92) were enlarged. All entropies significantly increased. Shannon (p = 0.0005; d = 0.98), Renyi (p = 0.0002; d = 1.02), Tsallis (p = 0.0004; d = 0.99), approximate (p = 0.0005; d = 0.97), and sample (p < 0.0001; d = 1.22). Detrended fluctuation analysis (DFA) (p = 0.0358) and Higuchi fractal dimension (HFD) (p = 0.15) were statistically inconsequential between the two groups.

CONCLUSIONS

HRV complexity in ALS subjects with different pulmonary capacities increased via chaotic global analysis, especially CFP5 and 3 out of 5 entropies.

Machine learning based congestive heart failure detection using feature importance ranking of multimodal features

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

Obstructive Sleep Apnea and Its Treatment in Aging: Effects on Alzheimer's disease Biomarkers, Cognition, Brain Structure and Neurophysiology

Here we review the impact of obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease (AD) pathogenesis, neuroanatomy, cognition and neurophysiology, and present the research investigating the effects of continuous positive airway pressure (CPAP) therapy. OSA is associated with an increase in AD markers amyloid-β and tau measured in cerebrospinal fluid (CSF), by Positron Emission Tomography (PET) and in blood serum. There is some evidence suggesting CPAP therapy normalizes AD biomarkers in CSF but since mechanisms for amyloid-β and tau production/clearance in humans are not completely understood, these findings remain preliminary. Deficits in the cognitive domains of attention, vigilance, memory and executive functioning are observed in OSA patients with the magnitude of impairment appearing stronger in younger people from clinical settings than in older community samples. Cognition improves with varying degrees after CPAP use, with the greatest effect seen for attention in middle age adults with more severe OSA and sleepiness. Paradigms in which encoding and retrieval of information are separated by periods of sleep with or without OSA have been done only rarely, but perhaps offer a better chance to understand cognitive effects of OSA than isolated daytime testing. In cognitively normal individuals, changes in EEG microstructure during sleep, particularly slow oscillations and spindles, are associated with biomarkers of AD, and measures of cognition and memory. Similar changes in EEG activity are reported in AD and OSA, such as "EEG slowing" during wake and REM sleep, and a degradation of NREM EEG microstructure. There is evidence that CPAP therapy partially reverses these changes but large longitudinal studies demonstrating this are lacking. A diagnostic definition of OSA relying solely on the Apnea Hypopnea Index (AHI) does not assist in understanding the high degree of inter-individual variation in daytime impairments related to OSA or response to CPAP therapy. We conclude by discussing conceptual challenges to a clinical trial of OSA treatment for AD prevention, including inclusion criteria for age, OSA severity, and associated symptoms, the need for a potentially long trial, defining relevant primary outcomes, and which treatments to target to optimize treatment adherence.

Optimizing Our Patients' Entropy Production as Therapy? Hypotheses Originating from the Physics of Physiology

Understanding how nature drives entropy production offers novel insights regarding patient care. Whilst energy is always preserved and energy gradients irreversibly dissipate (thus producing entropy), increasing evidence suggests that they do so in the most optimal means possible. For living complex non-equilibrium systems to create a healthy internal emergent order, they must continuously produce entropy over time. The Maximum Entropy Production Principle (MEPP) highlights nature's drive for non-equilibrium systems to augment their entropy production if possible. This physical drive is hypothesized to be responsible for the spontaneous formation of fractal structures in space (e.g., multi-scale self-similar tree-like vascular structures that optimize delivery to and clearance from an organ system) and time (e.g., complex heart and respiratory rate variability); both are ubiquitous and essential for physiology and health. Second, human entropy production, measured by heat production divided by temperature, is hypothesized to relate to both metabolism and consciousness, dissipating oxidative energy gradients and reducing information into meaning and memory, respectively. Third, both MEPP and natural selection are hypothesized to drive enhanced functioning and adaptability, selecting states with robust basilar entropy production, as well as the capacity to enhance entropy production in response to exercise, heat stress, and illness. Finally, a targeted focus on optimizing our patients' entropy production has the potential to improve health and clinical outcomes. With the implications of developing a novel understanding of health, illness, and treatment strategies, further exploration of this uncharted ground will offer value.

Heart rate variability in older workers during work under the Threshold Limit Values for heat exposure

BACKGROUND

The Threshold Limit Values (TLV) of the American Conference of Governmental and Industrial Hygienists indicate the levels of heat stress that all workers may be repeatedly exposed to without adverse health effects. In this study, we evaluated heart rate variability (HRV) during moderate-to-heavy work performed continuously or according to different TLV work-rest (WR) allocations in healthy physically active older workers.

METHODS

Nine healthy older (58 ± 5 years) males performed three different 120-minute conditions in accordance with TLV guidelines for moderate-to-heavy intensity work (360 W fixed rate of heat production) in different wet-bulb globe temperatures (WBGT): continuous cycling at 28°C WBGT (CON), as well as intermitted work performed at WR of 3:1 in 29°C WBGT (WR3:1), and at WR of 1:1 at 30°C (WR1:1). Rectal temperature and HRV (3-lead electrocardiogram [ECG]) were assessed throughout.

RESULTS

Coefficient of Variation, Poincaré SD2, and Shannon Entropy were decreased during the CON compared with the WR3:1 when core temperature exceeded 38°C and after 1 hour of continuous work (P < .05). Also, 4 of the 12 HRV indices studied were reduced at CON compared with WR1:1 after 2 hours of accumulated work time (P < .05). Participants worked longer before core temperature reached 38°C during the WR1:1 and the WR3:1, compared with CON (P < .05).

CONCLUSIONS

Incorporating breaks during moderate-to-heavy work in the heat for older adults can reduce autonomic stress and prolong the work performed at safe core temperature levels. The TLV WR1:1 provides increased cardiac protection for older workers, as compared with the CON and the WR3:1.

Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework

Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.