Detrended Fluctuation Analysis of Heart Rate Dynamics Is an Important Prognostic Factor in Patients with End-Stage Renal Disease Receiving Peritoneal Dialysis

The Clinical Significance and Application of Heart Rate Variability in Dialysis Patients: A Narrative Review

Autonomic nervous system (ANS) dysfunction is prevalent in end-stage kidney disease (ESKD) patients, carrying significant risks for morbidity and mortality. Heart rate variability (HRV) is a simple and non-invasive method to evaluate ANS functions and predict prognoses in specific patient populations. Since there is a lack of a clear understanding of the clinical significance of HRV in predicting prognoses in ESKD patients, an updated review on this topic is urgently warranted. The clinical significance of HRV in dialysis patients includes its associations with metabolic syndrome, nutritional status, intradialytic hypotension, vascular access failure, major adverse cardiovascular events, and mortality. These findings underscore the essential role of the autonomic reserve, which might denote the elevation of ANS activity as a response to external stimulus. Patients with a higher level of sympathetic activity at the resting stage, but who are unable to adequately elevate their sympathetic activity under stress might be susceptible to a worse outcome in critical circumstances. Further applications of HRV include HRV biofeedback, risk classification, and real-time HRV monitoring. Overall, HRV is an optimal tool for predicting prognoses in dialysis patients. Further study is encouraged in order to gain a clearer understanding of the clinical significance and application of HRV, and thereby enhance the care of ESKD patients.

Modifications of long-term heart rate variability produced in an experimental model of diet-induced metabolic syndrome

Metabolic syndrome (MetS) has been linked to a higher prevalence of cardiac arrhythmias, the most frequent being atrial fibrillation, but the mechanisms are not well understood. One possible underlying mechanism may be an abnormal modulation of autonomic nervous system activity, which can be quantified by analysing heart rate variability (HRV). Our aim was to investigate the modifications of long-term HRV in an experimental model of diet-induced MetS to identify the early changes in HRV and the link between autonomic dysregulation and MetS components. NZW rabbits were randomly assigned to control (n = 10) or MetS (n = 10) groups, fed 28 weeks with high-fat, high-sucrose diet. 24-hour recordings were used to analyse HRV at week 28 using time-domain, frequency-domain and nonlinear analyses. Time-domain analysis showed a decrease in RR interval and triangular index (Ti). In the frequency domain, we found a decrease in the low frequency band. Nonlinear analyses showed a decrease in DFA-α1 and DFA-α2 (detrended fluctuations analysis) and maximum multiscale entropy. The strongest association between HRV parameters and markers of MetS was found between Ti and mean arterial pressure, and Ti and left atrial diameter, which could point towards the initial changes induced by the autonomic imbalance in MetS.

Fractal complexity of daily physical activity and cognitive function in a midlife cohort

High stability of fluctuation in physiological patterns across fixed time periods suggest healthy fractal complexity, while greater randomness in fluctuation patterns may indicate underlying disease processes. The importance of fractal stability in mid-life remains unexplored. We quantified fractal regulation patterns in 24-h accelerometer data and examined associations with cognitive function in midlife. Data from 5097 individuals (aged 46) from the 1970 British Cohort Study were analyzed. Participants wore thigh-mounted accelerometers for seven days and completed cognitive tests (verbal fluency, memory, processing speed; derived composite z-score). Detrended fluctuation analysis (DFA) was used to examine temporal correlations of acceleration magnitude across 25 time scales (range: 1 min-10 h). Linear regression examined associations between DFA scaling exponents (DFAe) and each standardised cognitive outcome. DFAe was normally distributed (mean ± SD: 0.90 ± 0.06; range: 0.72-1.25). In males, a 0.10 increase in DFAe was associated with a 0.30 (95% Confidence Interval: 0.14, 0.47) increase in composite cognitive z-score in unadjusted models; associations were strongest for verbal fluency (0.10 [0.04, 0.16]). Associations remained in fully-adjusted models for verbal fluency only (0.06 [0.00, 0.12]). There was no association between DFA and cognition in females. Greater fractal stability in men was associated with better cognitive function. This could indicate mechanisms through which fractal complexity may scale up to and contribute to cognitive clinical endpoints.

Autonomic Nervous System Dysfunction in Peritoneal Dialysis Patients: An Underrecognized Cardiovascular Risk Factor?

BACKGROUND

In patients with end-stage kidney disease (ESKD) receiving peritoneal dialysis (PD), cardiovascular events represent the predominant cause of morbidity and mortality, with cardiac arrhythmias and sudden death being the leading causes of death in this population. Autonomic nervous system (ANS) dysfunction is listed among the non-traditional risk factors accounting for the observed high cardiovascular burden, with a plethora of complex and not yet fully understood pathophysiologic mechanisms being involved.

SUMMARY

In recent years, preliminary studies have investigated and confirmed the presence of ANS dysfunction in PD patients, while relevant results from cohort studies have linked ANS dysfunction with adverse clinical outcomes in these patients. In light of these findings, ANS dysfunction has been recently receiving wider consideration as an independent cardiovascular risk factor in PD patients. The aim of this review was to describe the mechanisms involved in the pathogenesis of ANS dysfunction in ESKD and particularly PD patients and to summarize the existing studies evaluating ANS dysfunction in PD patients.

KEY MESSAGES

ANS dysfunction in PD patients is related to multiple complex mechanisms that impair the balance between SNS/PNS, and this disruption represents a crucial intermediator of cardiovascular morbidity and mortality in this population.

Heart rate variability at rest and in response to stress: Comparative study between hemodialysis and peritoneal dialysis patients

Cardiac arrhythmias and sudden death are the leading causes of mortality in end-stage kidney disease (ESKD). Autonomic nervous system (ANS) dysfunction contributes to this arrhythmogenic background. This study compared heart rate variability (HRV) indices between hemodialysis (HD) and peritoneal dialysis (PD) patients, both at rest and in response to mental and physical stimulation maneuvers. Thirty-four HD and 34 PD patients matched for age, sex, and dialysis vintage, and 17 age- and sex-matched controls were studied. ANS function was examined by linear and non-linear HRV indices. Heart rate was recorded continuously (Finometer-PRO) at rest and during ANS maneuvers (orthostatic, mental-arithmetic, sit-to-stand, handgrip exercise tests). At rest, no significant differences between HD and PD were observed in HRV (root mean square of successive differences [RMSSD]: HD = 57.1 ± 81.1 vs PD = 69.6 ± 113.4 ms; P = 0.792), except for detrended fluctuation analysis (DFA-α1) (HD = 0.87 ± 0.40 vs PD = 0.70 ± 0.20; P = 0.047). DFA-α1 was significantly lower in PD than controls (1.00 ± 0.33; P < 0.05). All HRV indices during the mental-arithmetic test (RMSSD: HD = 128.2 ± 346.0 vs PD = 87.5 ± 150.0 ms; P = 0.893) and the physical stress tests were similar between HD and PD. The standard deviation along the line-of-identity (SD2)/the standard deviation perpendicular to the line-of-identity (SD1) ratio during mental-arithmetic was marginally lower in HD and significantly lower in PD than controls (PD = 1.31 ± 0.47 vs controls = 1.79 ± 0.64; P < 0.05). Both dialysis groups presented similar patterns in HRV responses during orthostatic and handgrip exercise tests. After the sit-to-stand, RMSSD, SD1, SD2, and DFA-α2 were higher compared to rest only in HD (RMSSD = 57.1 ± 81.1 vs 126.7 ± 185.7 ms; P = 0.028), suggesting a greater difficulty of HD patients in recovering normal ANS function in response to physical stress. In conclusion, HRV indices at rest and after mental and physical stimulation did not differ between HD and PD; however, the ANS responses following the sit-to-stand test were more impaired in HD. These findings suggest that ANS dysfunction is not largely affected by dialysis modality, but small differences in normal ANS recovery may exist.

Fractal complexity alternations in paroxysmal atrial fibrillation patients with and without recurrence after pulmonary vein isolation

BACKGROUND

Pulmonary vein isolation (PVI) is a cornerstone therapy for paroxysmal atrial fibrillation (PAF). The variations in nonlinear heart rate variability (HRV) between patients with and without recurrences remain unclear. We aimed to characterize the nonlinear HRV before and after PVI in patients with and without recurrence.

METHODS

Twenty-five drug-refractory PAF patients (56.0 ± 9.1 years old, 20 males) who received PVI were enrolled. Holter electrocardiography were performed before, 1-3, and 6-12 months after PVI. After 8.2 ± 2.5 months of follow-ups after PVI, patients were divided into two groups: the recurrence (n = 8) and non-recurrence (n = 17) groups. Linear and nonlinear HRV variables were analyzed, including the Poincaré Plot analysis and the Detrended Fluctuation Analysis (DFA).

RESULTS

The non-recurrence group, but not the recurrence group, had decreased high-frequency component (HF), the root mean square of successive RR interval differences (RMSSD), and the Poincaré Plot index SD1 1-3 months after PVI and increased DFAslope2 6-12 months after PVI. The non-recurrence group's LF/HF ratio and DFAslope1 decreased significantly 1-3 and 6-12 months after PVI, respectively, whereas there was no significant change in the recurrence group after PVI.

CONCLUSIONS

Significantly reduced vagal tone 1-3 months after PVI, increased long-term fractal complexity 6-12 months after PVI, and decreased sympathetic tone as well as short-term fractal complexity 1-3 and 6-12 months after PVI led to a better AF-free survival after PVI. These findings suggest that neuromodulation and heart rate dynamics play crucial roles in AF recurrence following PVI.

HEART RATE VARIABILITY PARAMETERS WERE NOT ASSOCIATED WITH 30-DAY ALL-CAUSE MORTALITY IN INTENSIVE CARE UNIT PATIENTS WITH OR WITHOUT ATRIAL FIBRILLATION: A RETROSPECTIVE STUDY OF THE MIMIC-IV DATABASE

ABSTRACT

Objective: Our study aims to evaluate the association between heart rate variability (HRV) and short- and long-term prognosis in patients admitted to intensive care unit (ICU). Methods and Results: Adult patients continuously monitored for over 24 h in ICUs from the the American Medical Information Mart for Intensive Care (MIMIC)-IV Waveform Database were recruited in our study. Twenty HRV-related variables (8 time domain, 6 frequency domain, and 6 nonlinear variables) were calculated based on RR intervals. The association between HRV and all-cause mortality was assessed. Ninety-three patients met the inclusion criteria and were classified into atrial fibrillation (AF) and sinus rhythm (SR) groups, which were further divided into 30-day survivor group and nonsurvivor\groups based on their survival status. The 30-day all-cause mortality rates in AF and SR groups were 36.3% and 14.6%, respectively. All the time domain, frequency domain, and nonlinear HRV parameters did not differ significantly between survivors and nonsurvivors with or without AF (all P > 0.05). Presence of renal failure, malignancy, and elevated blood urea nitrogen level were associated with increased 30-day all-cause mortality in SR patients, while presence of sepsis, infection, higher platelet count, and magnesium level were associated with increased 30-day all-cause mortality in AF patients. Conclusions: Heart rate variability variables were not associated with increased 30-day all-cause mortality in ICU patients with or without AF.

Efficacy of two doses of external counterpulsation (ECP) on glycemic control in people with type 2 diabetes mellitus: A randomized SHAM-controlled trial

AIMS

To determine the efficacy of two doses of external counterpulsation (ECP) on glycemic control in people with type 2 diabetes mellitus (T2D), and any persistent benefits 7 weeks following treatment.

METHODS

50 participants with T2D were randomly assigned to either 1) 20x45-minute ECP sessions over 7 weeks (ECP₄₅), 2) 20x30-minute ECP sessions over 7 weeks (ECP₃₀) or 3) SHAM control. Outcomes were assessed at baseline, after 7 weeks of the intervention and 7 weeks after the interventions finished. Efficacy was determined from changes in HbA_1c.

RESULTS

After 7 weeks, there were significant between-group differences, with ECP₄₅ lowering HbA_1c compared to SHAM (mean [95% CI] -0.7 [-0.1 to -1.3] %; -7 [-1 to -15] mmol/mol). Within group changes were; ECP₄₅ (mean±SD -0.8±0.8%; -8±8 mmol/mol), ECP₃₀ (-0.2±0.5%; -2±6 mmol/mol) and SHAM (-0.1±0.9%; -1±10 mmol/mol). HbA_1c in the ECP₄₅ group remained lower 7 weeks after completing the intervention; ECP₄₅ (7.0±1.1%; 53±26 mmol/mol), ECP₃₀ (7.7±1.4%; 60±16 mmol/mol) and SHAM (7.7±1.0%; 60±10 mmol/mol).

CONCLUSIONS

In people with T2D, ECP₄₅ for 7 weeks improved glycemic control when compared to ECP₃₀ and a SHAM control group.

The prognostic value of intraoperative HRV during anesthesia in patients presenting for non-cardiac surgery

OBJECTIVE

To examine the prognostic value of HRV measurements during anesthesia for postoperative clinical outcomes prediction using machine learning models.

DATA SOURCES

VitalDB, a comprehensive database of 6388 surgical patients admitted to Seoul National University Hospital.

ELIGIBILITY CRITERIA FOR STUDY SELECTION

Cases with ECG lead II recording duration of less than one hour were excluded. Cases with more than 20% of missing HRV measurements were also excluded. A total of 5641 cases were eligible for the analyses.

METHODS

Six machine learning models including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Trees (GBT), Extreme Gradient Boosting (XGB), and an ensemble of the five baseline models were developed to predict postoperative clinical outcomes. The prediction models were trained using only clinical information, and using both clinical information and HRV features, respectively. Feature importance based on the SHAP method was used to assess the contribution of the HRV measurements to the outcome predictions. Subgroup analysis was also performed to evaluate the risk association between postoperative ICU stay and various HRV measurements such as heart rate, low-frequency power (LFP), and short-term fluctuation DFA [Formula: see text].

RESULT

The final cohort included 5641 unique cases, among whom 4678 (83.0%) cases had ages over 40, 2877 (51.0%) were male, 1073 (19.0%) stayed in ICU after surgery, 52 (0.9%) suffered in-hospital death, and 3167(56.1%) had a total length of hospital stay longer than 7 days. In the final test set, the highest AUROC performance with only clinical information was 0.79 for postoperative ICU stay, 0.58 for in-hospital mortality, and 0.76 for the total length of hospital stay prediction. Importantly, using both clinical information and HRV features, the AUROC performance was 0.83, 0.70, and 0.76 for the three clinical outcome predictions, respectively. Subgroup analysis found that patients with an average heart rate higher than 70, low-frequency power (LFP) < 33, and short-term fluctuation DFA [Formula: see text] < 0.95 during anesthesia, had a significantly higher risk of entering the ICU after surgery.

CONCLUSION

This study suggested that HRV measurements during anesthesia are feasible and effective for predicting postoperative clinical outcomes.

DEGBOE: Discrete time Evolution modeling of Gene mutation through Bayesian inference using qualitative Observation of mutation Events

An important aspect of cancer progression concerns the way in which gene mutations accumulate in cellular lineages. Comprehensive efforts into cataloging cancer genes have revealed that tumors demonstrate variability in genes that accumulate mutations which depend on the presence or absence of other mutations. However, understanding the stochastic process by which mutations arise across the genome is an important open problem of this nature in biology due to modeling discrete variate time-series is the most challenging, and, as yet, least well-developed of all areas of research in time-series. In this paper, a DEGBOE framework is proposed to model the mutation time-series given the sequence data of the gene mutations. The method relates the discrete-time, nonlinear and nonstationary series of gene mutations to the time-varying autoregressive moving average model. It presents the observation as a nonlinear function dependent on two variables: gene mutation, and gene-gene interactions characterizing the effects of the varying presence or absence of other gene mutations on a mutations' occurrence and evolution. DEGBOE is applied to model the dynamics of frequently mutated genes in lung cancer, includingEGFR,KRAS, and TP53. The results of the model are analyzed and compared to the original simulated data of theDNAwalk, and experimental lung cancer mutations data. It identifies the driver role of TP53 mutations in lung cancer progression.

Cardio-Hypothalamic-Pituitary Coupling during Rest and in Response to Exercise

The objective of this study was to examine cardio hypothalamic-pituitary coupling and to better understand how the temporal relations between these systems are altered during rest and exercise conditions. An intensive within subjects study design was used. Seven adult males completed two visits, each consisting of either a 24 h period of complete rest or a 24 h period containing a high-intensity exercise bout. An intravenous catheter was used to collect serum samples every 10 min throughout the 24 h period (i.e., 145 samples/person/condition) to assess growth hormone (GH) dynamics throughout the 24 h period. Cardiac dynamics were also collected throughout the 24 h period and epoched into 3 min windows every 10 min, providing serial short-time measurements of heart rate variability (HRV) concurrent to the GH sampling. The standard deviation of the normal RR interval (SDNN), the root mean square of successive differences (rMSSD), and sample entropy (SampEn) was calculated for each epoch and used to create new profiles. The dynamics of these profiles were individually quantified using SampEn and recurrence quantification analysis (RQA). To address our central question, the coupling between these profiles with GH was assessed using cross-SampEn and cross-RQA (cRQA). A comparison between the epoched HRV profiles indicated a main effect between profiles for sample entropy (p < 0.001) and several measures from RQA. An interaction between profile and condition was observed for cross-SampEn (p = 0.04) and several measures from cRQA. These findings highlight the potential application of epoched HRV to assess changes in cardiac dynamics, with specific applications to assessing cardio hypothalamic-pituitary coupling.

Understanding Heart Rate Reactions to Post-Traumatic Stress Disorder (PTSD) Among Veterans: A Naturalistic Study

OBJECTIVE

We collected naturalistic heart rate data from veterans diagnosed with post-traumatic stress disorder (PTSD) to investigate the effects of various factors on heart rate.

BACKGROUND

PTSD is prevalent among combat veterans in the United States. While a positive correlation between PTSD and heart rate has been documented, specific heart rate profiles during the onset of PTSD symptoms remain unknown.

METHOD

Veterans were recruited during five cycling events in 2017 and 2018 to record resting and activity-related heart rate data using a wrist-worn device. The device also logged self-reported PTSD hyperarousal events. Regression analyses were performed on demographic and behavioral covariates including gender, exercise, antidepressants, smoking habits, sleep habits, average heart rate during reported hyperarousal events, age, glucocorticoids consumption, and alcohol consumption. Heart rate patterns during self-reported PTSD hyperarousal events were analyzed using Auto Regressive Integrated Moving Average (ARIMA). Heart rate data were also compared to an open-access non-PTSD representative case.

RESULTS

Of 99 veterans with PTSD, 91 participants reported at least one hyperarousal event, with a total of 1023 events; demographic information was complete for 38 participants who formed the subset for regression analyses. The results show that factors including smoking, sleeping, gender, and medication significantly affect resting heart rate. Moreover, unique heart rate patterns associated with PTSD symptoms in terms of stationarity, autocorrelation, and fluctuation characteristics were identified.

CONCLUSION

Our findings show distinguishable heart rate patterns and characteristics during PTSD hyperarousal events.

APPLICATION

These findings show promise for future work to detect the onset of PTSD symptoms.

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 Value of Heart Rhythm Complexity in Identifying High-Risk Pulmonary Hypertension Patients

Pulmonary hypertension (PH) is a fatal disease—even with state-of-the-art medical treatment. Non-invasive clinical tools for risk stratification are still lacking. The aim of this study was to investigate the clinical utility of heart rhythm complexity in risk stratification for PH patients. We prospectively enrolled 54 PH patients, including 20 high-risk patients (group A; defined as WHO functional class IV or class III with severely compromised hemodynamics), and 34 low-risk patients (group B). Both linear and non-linear heart rate variability (HRV) variables, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE), were analyzed. In linear and non-linear HRV analysis, low frequency and high frequency ratio, DFAα1, MSE slope 5, scale 5, and area 6–20 were significantly lower in group A. Among all HRV variables, MSE scale 5 (AUC: 0.758) had the best predictive power to discriminate the two groups. In multivariable analysis, MSE scale 5 (p = 0.010) was the only significantly predictor of severe PH in all HRV variables. In conclusion, the patients with severe PH had worse heart rhythm complexity. MSE parameters, especially scale 5, can help to identify high-risk PH patients.

Cardiac Autonomic Response to Active Standing in Calcific Aortic Valve Stenosis

Aortic stenosis is a progressive heart valve disorder characterized by calcification of the leaflets. Heart rate variability (HRV) analysis has been proposed for assessing the heart response to autonomic activity, which is documented to be altered in different cardiac diseases. The objective of the study was to evaluate changes of HRV in patients with aortic stenosis by an active standing challenge. Twenty-two volunteers without alterations in the aortic valve (NAV) and twenty-five patients diagnosed with moderate and severe calcific aortic valve stenosis (AVS) participated in this cross-sectional study. Ten minute electrocardiograms were performed in a supine position and in active standing positions afterwards, to obtain temporal, spectral, and scaling HRV indices: mean value of all NN intervals (meanNN), low-frequency (LF) and high-frequency (HF) bands spectral power, and the short-term scaling indices (α1 and αsign1). The AVS group showed higher values of LF, LF/HF and αsign1 compared with the NAV group at supine position. These patients also expressed smaller changes in meanNN, LF, HF, LF/HF, α1, and αsign1 between positions. In conclusion, we confirmed from short-term recordings that patients with moderate and severe calcific AVS have a decreased cardiac parasympathetic supine response and that the dynamic of heart rate fluctuations is modified compared to NAV subjects, but we also evidenced that they manifest reduced autonomic adjustments caused by the active standing challenge.

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.

Heart rhythm complexity as predictors for the prognosis of end-stage renal disease patients undergoing hemodialysis

BACKGROUND

Heart rhythm complexity, a measure of heart rate dynamics and a risk predictor in various clinical diseases, has not been systematically studied in patients with end-stage renal disease. The aim of this study is to investigate the heart rhythm complexity and its prognostic value for mortality in end-stage renal disease patients undergoing hemodialysis.

METHODS

To assess heart rhythm complexity and conventional heart rate variability measures, 4-h continuous electrocardiography for a retrospective cohort of 202 ostensibly healthy control subjects and 51 hemodialysis patients with end-stage renal disease were analyzed. Heart rhythm complexity was quantified by the complexity index from the measurement of the multiscale entropy profile.

RESULTS

During a follow-up of 13 months, 8 people died in the patient group. Values of either traditional heart rate variability measurements or complexity indices were found significantly lower in patients than those in healthy controls. In addition, the complexity indices (Area 1-5, Area 6-15 and Area 6-20) in the mortality group were significantly lower than those in the survival group, while there were no significant differences in traditional heart rate variability parameters between the two groups. In receiver operating characteristic curve analysis, Area 6-20 (AUC = 0.895, p < 0.001) showed the strongest predictive power between mortality and survival groups.

CONCLUSION

The results suggest that heart rhythm complexity is impaired for patients with end-stage renal disease. Furthermore, the complexity index of heart rate variability quantified by multiscale entropy may be a powerful independent predictor of mortality in end-stage renal disease patients undergoing hemodialysis.

Fractal Complexity of Daily Physical Activity Patterns Differs With Age Over the Life Span and Is Associated With Mortality in Older Adults

BACKGROUND

Accelerometers are included in a wide range of devices that monitor and track physical activity for health-related applications. However, the clinical utility of the information embedded in their rich time-series data has been greatly understudied and has yet to be fully realized. Here, we examine the potential for fractal complexity of actigraphy data to serve as a clinical biomarker for mortality risk.

METHODS

We use detrended fluctuation analysis (DFA) to analyze actigraphy data from the National Health and Nutrition Examination Survey (NHANES; n = 11,694). The DFA method measures fractal complexity (signal self-affinity across time-scales) as correlations between the amplitude of signal fluctuations in time-series data across a range of time-scales. The slope, α, relating the fluctuation amplitudes to the time-scales over which they were measured describes the complexity of the signal.

RESULTS

Fractal complexity of physical activity (α) decreased significantly with age (p = 1.29E-6) and was lower in women compared with men (p = 1.79E-4). Higher levels of moderate-to-vigorous physical activity in older adults and in women were associated with greater fractal complexity. In adults aged 50-79 years, lower fractal complexity of activity (α) was associated with greater mortality (hazard ratio = 0.64; 95% confidence interval = 0.49-0.82) after adjusting for age, exercise engagement, chronic diseases, and other covariates associated with mortality.

CONCLUSIONS

Wearable accelerometers can provide a noninvasive biomarker of physiological aging and mortality risk after adjusting for other factors strongly associated with mortality. Thus, this fractal analysis of accelerometer signals provides a novel clinical application for wearable accelerometers, advancing efforts for remote monitoring of physiological health by clinicians.

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

Heart Rhythm Complexity Predicts Long-Term Cardiovascular Outcomes in Peritoneal Dialysis Patients: A Prospective Cohort Study

Background Cardiovascular disease is the leading cause of morbidity and mortality in patients with end-stage renal disease. Heart rhythm complexity analysis has been shown to be useful in predicting outcomes in various diseases; however, data on patients with end-stage renal disease are limited. In this study, we analyzed the association between heart rhythm complexity and long-term cardiovascular outcomes in patients with end-stage renal disease receiving peritoneal dialysis. Methods and Results We prospectively enrolled 133 patients receiving peritoneal dialysis and analyzed linear heart rate variability and heart rhythm complexity variables including detrended fluctuation analysis (DFA) and multiscale entropy. The primary outcome was cardiovascular mortality, and the secondary outcome was the occurrence of major adverse cardiovascular events. After a median of 6.37 years of follow-up, 21 patients (22%) died from cardiovascular causes. These patients had a significantly lower low-frequency band of heart rate variability, low/high-frequency band ratio, total power band of heart rate variability, heart rate turbulence slope, deceleration capacity, short-term DFA (DFAα1); and multiscale entropy slopes 1 to 5, scale 5, area 1 to 5, and area 6 to 20 compared with the patients who did not die from cardiovascular causes. Time-dependent receiver operating characteristic curve analysis showed that DFAα1 had the greatest discriminatory power for cardiovascular mortality (area under the curve: 0.763) and major adverse cardiovascular events (area under the curve: 0.730). The best cutoff value for DFAα1 was 0.98 to predict both cardiovascular mortality and major adverse cardiovascular events. Multivariate Cox regression analysis showed that DFAα1 (hazard ratio: 0.076; 95% CI, 0.016-0.366; P=0.001) and area 1 to 5 (hazard ratio: 0.645; 95% CI, 0.447-0.930; P=0.019) were significantly associated with cardiovascular mortality. Conclusions Heart rhythm complexity appears to be a promising noninvasive tool to predict long-term cardiovascular outcomes in patients receiving peritoneal dialysis.

Extended Central Tendency Measure and difference plot for heart rate variability analysis

Heart rate variability (HRV) is a non-invasive alternative to analyze the role of the autonomic nervous system (ANS) on heart functioning. Many tools have been developed to analyze collected cardiac data. Among them, the Central Tendency Measure (CTM) is a quantitative method for variability analysis of RR intervals. The values of the CTM must be between 0 and 1 (inclusive) for different radius, which follows the intrinsic characteristics of each time series. Using the conventional CTM, the successive differences of the time series may be calculated, and it can classify and differentiate the disturbances in the ANS involving HRV. This method was extended (e-CTM) to analyze the differences between RR interval time series. In this extension, a new parameter is added, which allows analysis of long time intervals, instead of successive and adjacent RR intervals. The ability of the e-CTM to differentiate the groups of the RR interval time series was verified with 145 RR interval time series divided into three groups: subjects with congestive heart failure, healthy subjects, and nurses during one hour of their workday. Results evidence that the new parameter added differentiates the group with pathology (and subsequent impairment of ANS) and group under stress at work (temporary impairment of ANS). These results suggest that the e-CTM is capable of detection long-term variations in the HRV according to the ANS impairment.

Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload

In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1-5.8 Hz), θ2 (5.9-7.4 Hz), β1 (13-19.9 Hz), and β2 (20-25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future.

Sudden Cardiac Death in Dialysis: Arrhythmic Mechanisms and the Value of Non-invasive Electrophysiology

Sudden Cardiac Death (SCD) is the leading cause of cardiovascular death in dialysis patients. This review discusses potential underlying arrhythmic mechanisms of SCD in the dialysis population. It examines recent evidence from studies using implantable loop recorders and from electrophysiological studies in experimental animal models of chronic kidney disease. The review summarizes advances in the field of non-invasive electrophysiology for risk prediction in dialysis patients focusing on the predictive value of the QRS-T angle and of the assessments of autonomic imbalance by means of heart rate variability analysis. Future research directions in non-invasive electrophysiology are identified to advance the understanding of the arrhythmic mechanisms. A suggestion is made of incorporation of non-invasive electrophysiology procedures into clinical practice. Key Concepts: - Large prospective studies in dialysis patients with continuous ECG monitoring are required to clarify the underlying arrhythmic mechanisms of SCD in dialysis patients. - Obstructive sleep apnoea may be associated with brady-arrhythmias in dialysis patients. Studies are needed to elucidate the burden and impact of sleeping disorders on arrhythmic complications in dialysis patients. - The QRS-T angle has the potential to be used as a descriptor of uremic cardiomyopathy. - The QRS-T angle can be calculated from routine collected surface ECGs. Multicenter collaboration is required to establish best methodological approach and normal values. - Heart Rate Variability provides indirect assessment of cardiac modulation that may be relevant for cardiac risk prediction in dialysis patients. Short-term recordings with autonomic provocations are likely to overcome the limitations of out of hospital 24-h recordings and should be prospectively assessed.

Long-range temporal correlation in Auditory Brainstem Responses to Spoken Syllable/da/

The speech auditory brainstem response (sABR) is an objective clinical tool to diagnose particular impairments along the auditory brainstem pathways. We explore the scaling behavior of the brainstem in response to synthetic /da/ stimuli using a proposed pipeline including Multifractal Detrended Moving Average Analysis (MFDMA) modified by Singular Value Decomposition. The scaling exponent confirms that all normal sABR are classified into the non-stationary process. The average Hurst exponent is H = 0:77 ± 0:12 at 68% confidence interval indicating long-range correlation which shows the first universality behavior of sABR. Our findings exhibit that fluctuations in the sABR series are dictated by a mechanism associated with long-term memory of the dynamic of the auditory system in the brainstem level. The q-dependency of h(q) demonstrates that underlying data sets have multifractal nature revealing the second universality behavior of the normal sABR samples. Comparing Hurst exponent of original sABR with the results of the corresponding shuffled and surrogate series, we conclude that its multifractality is almost due to the long-range temporal correlations which are devoted to the third universality. Finally, the presence of long-range correlation which is related to the slow timescales in the subcortical level and integration of information in the brainstem network is confirmed.

Quantification of Beat-To-Beat Variability of Action Potential Durations in Langendorff-Perfused Mouse Hearts

Background: Beat-to-beat variability in action potential duration (APD) is an intrinsic property of cardiac tissue and is altered in pro-arrhythmic states. However, it has never been examined in mice.

Methods: Left atrial or ventricular monophasic action potentials (MAPs) were recorded from Langendorff-perfused mouse hearts during regular 8 Hz pacing. Time-domain, frequency-domain and non-linear analyses were used to quantify APD variability.

Results: Mean atrial APD (90% repolarization) was 23.5 ± 6.3 ms and standard deviation (SD) was 0.9 ± 0.5 ms (n = 6 hearts). Coefficient of variation (CoV) was 4.0 ± 1.9% and root mean square (RMS) of successive differences in APDs was 0.3 ± 0.2 ms. The peaks for low- and high-frequency were 0.7 ± 0.5 and 2.7 ± 0.9 Hz, respectively, with percentage powers of 39.0 ± 20.5 and 59.3 ± 22.9%. Poincaré plots of APD_n+1 against APD_n revealed ellipsoid shapes. The ratio of the SD along the line-of-identity (SD2) to the SD perpendicular to the line-of-identity (SD1) was 8.28 ± 4.78. Approximate and sample entropy were 0.57 ± 0.12 and 0.57 ± 0.15, respectively. Detrended fluctuation analysis revealed short- and long-term fluctuation slopes of 1.80 ± 0.15 and 0.85 ± 0.29, respectively. When compared to atrial APDs, ventricular APDs were longer (ANOVA, P < 0.05), showed lower mean SD and CoV but similar RMS of successive differences in APDs and showed lower SD2 (P < 0.05). No difference in the remaining parameters was observed.

Conclusion: Beat-to-beat variability in APD is observed in mouse hearts during regular pacing. Atrial MAPs showed greater degree of variability than ventricular MAPs. Non-linear techniques offer further insights on short-term and long-term variability and signal complexity.

Complexity, Regularity and Non-Linear Behavior in Human Eye Movements: Analyzing the Dynamics of Gaze in Virtual Sailing Programs

The non-linear analysis of the behavior of biological signals in humans is studied from different scientific disciplines. The aim of this study was to analyze the possible non-linear behavior present in eye movements during eye-tracking tasks in simulated sailing. Thirty young sailors were selected. Fuzzy entropy and detrended fluctuation analyses were applied to quantify the regularity and complexity of eye movements. The results show that neither experience nor ranking affect the regularity or the complexity of eye movement positions or velocities. Younger age is related to more regular visual behavior. At younger ages, eye positions present more complex behavior. Eye positions show more complex behavior than eye velocities. This complexity would allow for a more functional exploration of the environment by sailors. Eye movement velocity presents the greatest irregularity, with significantly higher values than eye movement position. This irregularity would facilitate the visual perception of the environment. All these findings could be related to the sailors’ functional behavior, based on complexity and stability, which has been associated with the ability of human beings to adapt to the environment.

Fractal analysis of heart rate variability as a predictor of mortality: A systematic review and meta-analysis

Previous studies have suggested benefits of applying fractal analysis to intervals between R waves in electrocardiography as an additional prognostic marker. The aim of this study was to investigate whether fractal analysis can provide an independent predictor of cardiac mortality or all-cause mortality. Prognostic cohort studies reporting fractal heart rate variability results from 24-h Holter monitor recordings were selected for comparison. Populations were subdivided into four groups-post-myocardial infarction, left ventricular dysfunction, other cardiac, and non-cardiac patients-and analysed using ANOVA, Forest plots (using pooled mean difference), and Funnel plots. The most significant mean differences were recorded in short-term fractal self-similarity (α₁) (-0.17, 95% CI [-0.21, -0.13], p < 0.00001) and the traditional measure called standard deviation of NN intervals (SDNN) (-13.31, 95% CI [-18.89, -7.73], p < 0.00001) between the deceased and survivor groups. Fractal measures of long-term fractal self-similarity (α₂), 1/f scaling (β), and traditional heart rate variability measures of high frequency to low frequency ratio show promise. This review indicated that fractal measure α₁ and traditional measure SDNN could be potential predictors of mortality, but require further assessment to determine appropriate thresholds for clinical significance and additional targeted prognostic studies to properly define their applicability as prognostic markers. Therefore, clinicians should interpret fractal and traditional measures with caution since such measures have yet to be fully described as biomarkers for clinical application.

A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal

Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis.

Sign and magnitude scaling properties of heart rate variability in patients with end-stage renal failure: Are these properties useful to identify pathophysiological adaptations?

The scaling properties of heart rate variability data are reliable dynamical features to predict mortality and for the assessment of cardiovascular risk. The aim of this manuscript was to determine if the scaling properties, as provided by the sign and magnitude analysis, can be used to differentiate between pathological changes and those adaptations basically introduced by modifications of the mean heart rate in distinct manoeuvres (active standing or hemodialysis treatment, HD), as well as clinical conditions (end stage renal disease, ESRD). We found that in response to active standing, the short-term scaling index (α₁) increased in healthy subjects and in ESRD patients only after HD. The sign short-term scaling exponent (α_1sign) increased in healthy subjects and ESRD patients, showing a less anticorrelated behavior in active standing. Both α₁ and α_1sign did show covariance with the mean heart rate in healthy subjects, while in ESRD patients, this covariance was observed only after HD. A reliable estimation of the magnitude short-term scaling exponent (α_1magn) required the analysis of time series with a large number of samples (>3000 data points). This exponent was similar for both groups and conditions and did not show covariance with the mean heart rate. A surrogate analysis confirmed the presence of multifractal properties (α_1magn > 0.5) in the time series of healthy subjects and ESDR patients. In conclusion, α₁ and α_1sign provided insights into the physiological adaptations during active standing, which revealed a transitory impairment before HD in ESRD patients. The presence of multifractal properties indicated that a reduced short-term variability does not necessarily imply a declined regulatory complexity in these patients.

Consistency of detrended fluctuation analysis

The scaling function F(s) in detrended fluctuation analysis (DFA) scales as F(s)∼s^{H} for stochastic processes with Hurst exponent H. This scaling law is proven for stationary stochastic processes with 0<H<1 and nonstationary stochastic processes with 1<H<2. For H<0.5, it is observed that the asymptotic (power-law) autocorrelation function (ACF) scales as ∼s^{1/2}. It is also demonstrated that the fluctuation function in DFA is equal in expectation to (i) a weighted sum of the ACF and (ii) a weighted sum of the second-order structure function. These results enable us to compute the exact finite-size bias for signals that are scaling and to employ DFA in a meaningful sense for signals that do not exhibit power-law statistics. The usefulness is illustrated by examples where it is demonstrated that a previous suggested modified DFA will increase the bias for signals with Hurst exponents 1<H≤1.5. As a final application of these developments, an estimator F[over ̂](s) is proposed. This estimator can handle missing data in regularly sampled time series without the need of interpolation schemes. Under mild regularity conditions, F[over ̂](s) is equal in expectation to the fluctuation function F(s) in the gap-free case.

Effects of Orthostatism and Hemodialysis on Mean Heart Period and Fractal Heart Rate Properties of Chronic Renal Failure Patients

The aim of this work was to evaluate the short-term fractal index (α₁ ) of heart rate variability (HRV) in chronic renal failure (CRF) patients by identifying the effects of orthostatism and hemodialysis (HD), and by evaluating the correlation between α₁ and the mean RR interval from sinus beats (meanNN). HRV time series were derived from ECG data of 19 CRF patients and 20 age-matched healthy subjects obtained at supine and orthostatic positions (lasting 5 min each). Data from CRF patients were collected before and after HD. α₁ was calculated from each time series and compared by analysis of variance. Pearson's correlations between meanNN and α₁ were calculated using the data from both positions by considering three groups: healthy subjects, CRF before HD and CRF after HD. At supine position, α₁ of CRF patients after HD (1.17 ± 0.30) was larger (P < 0.05) than in healthy subjects (0.89 ± 0.28) but not before HD (1.10 ± 0.34). α₁ increased (P < 0.05) in response to orthostatism in healthy subjects (1.29 ± 0.26) and CRF patients after HD (1.34 ± 0.31), but not before HD (1.25 ± 0.37). Whereas α₁ was correlated (P < 0.05) with the meanNN of healthy subjects (r = -0.562) and CRF patients after HD (r = -0.388), no significance in CRF patients before HD was identified (r = 0.003). Multiple regression analysis confirmed that α₁ was mainly predicted by the orthostatic position (in all groups) and meanNN (healthy subjects and patients after HD), showing no association with the renal disease condition in itself. In conclusion, as in healthy subjects, α₁ of CRF patients correlates with meanNN after HD (indicating a more irregular-like HRV behavior at slower heart rates). This suggests that CRF patients with stable blood pressure preserve a regulatory adaptability despite a shifted setting point of the heart period (i.e., higher heart rate) in comparison with healthy subjects.

The role of sympathetic and vagal cardiac control on complexity of heart rate dynamics

Analysis of heart rate variability (HRV) by nonlinear approaches has been gaining interest due to their ability to extract additional information from heart rate (HR) dynamics that are not detectable by traditional approaches. Nevertheless, the physiological interpretation of nonlinear approaches remains unclear. Therefore, we propose long-term (60 min) protocols involving selective blockade of cardiac autonomic receptors to investigate the contribution of sympathetic and parasympathetic function upon nonlinear dynamics of HRV. Conscious male Wistar rats had their electrocardiogram (ECG) recorded under three distinct conditions: basal, selective (atenolol or atropine), or combined (atenolol plus atropine) pharmacological blockade of autonomic muscarinic or β1-adrenergic receptors. Time series of RR interval were assessed by multiscale entropy (MSE) and detrended fluctuation analysis (DFA). Entropy over short (1 to 5, MSE1–5) and long (6 to 30, MSE6–30) time scales was computed, as well as DFA scaling exponents at short (αshort, 5 ≤ n ≤ 15), mid (αmid, 30 ≤ n ≤ 200), and long (αlong, 200 ≤ n ≤ 1,700) window sizes. The results show that MSE1–5 is reduced under atropine blockade and MSE6–30 is reduced under atropine, atenolol, or combined blockade. In addition, while atropine expressed its maximal effect at scale six, the effect of atenolol on MSE increased with scale. For DFA, αshort decreased during atenolol blockade, while the αmid increased under atropine blockade. Double blockade decreased αshort and increased αlong. Results with surrogate data show that the dynamics during combined blockade is not random. In summary, sympathetic and vagal control differently affect entropy (MSE) and fractal properties (DFA) of HRV. These findings are important to guide future studies.

NEW & NOTEWORTHY Although multiscale entropy (MSE) and detrended fluctuation analysis (DFA) are recognizably useful prognostic/diagnostic methods, their physiological interpretation remains unclear. The present study clarifies the effect of the cardiac autonomic control on MSE and DFA, assessed during long periods (1 h). These findings are important to help the interpretation of future studies.