Nonlinear heart rate variability features for real-life stress detection. Case study : students under stress due to university examination

Background: This study investigates the variations of Heart Rate Variability (HRV) due to a real-life stressor and proposes a classifier based on nonlinear features of HRV for automatic stress detection. Methods: 42 students volunteered to participate to the study about HRV and stress. For each student, two recordings were performed: one during an on-going university examination, assumed as a real-life stressor, and one after holidays. Nonlinear analysis of HRV was performed by using Poincaré Plot, Approximate Entropy, Correlation dimension, Detrended Fluctuation Analysis, Recurrence Plot. For statistical comparison, we adopted the Wilcoxon Signed Rank test and for development of a classifier we adopted the Linear Discriminant Analysis (LDA). Results: Almost all HRV features measuring heart rate complexity were significantly decreased in the stress session. LDA generated a simple classifier based on the two Poincaré Plot parameters and Approximate Entropy, which enables stress detection with a total classification accuracy, a sensitivity and a specificity rate of 90%, 86%, and 95% respectively. Conclusions: The results of the current study suggest that nonlinear HRV analysis using short term ECG recording could be effective in automatically detecting real-life stress condition, such as a university examination

Increased markers of cardiac vagal activity in leucine-rich repeat kinase 2-associated Parkinson's disease.

PurposeCardiac autonomic dysfunction manifests as reduced heart rate variability (HRV) in idiopathic Parkinson's disease (PD), but no significant reduction has been found in PD patients who carry the LRRK2 mutation. Novel HRV features have not been investigated in these individuals. We aimed to assess cardiac autonomic modulation through standard and novel approaches to HRV analysis in individuals who carry the LRRK2 G2019S mutation.MethodsShort-term electrocardiograms were recorded in 14 LRRK2-associated PD patients, 25 LRRK2-non-manifesting carriers, 32 related non-carriers, 20 idiopathic PD patients, and 27 healthy controls. HRV measures were compared using regression modeling, controlling for age, sex, mean heart rate, and disease duration. Discriminant analysis highlighted the feature combination that best distinguished LRRK2-associated PD from controls.ResultsBeat-to-beat and global HRV measures were significantly increased in LRRK2-associated PD patients compared with controls (e.g., deceleration capacity of heart rate: p = 0.006) and idiopathic PD patients (e.g., 8th standardized moment of the interbeat interval distribution: p = 0.0003), respectively. LRRK2-associated PD patients also showed significantly increased irregularity of heart rate dynamics, as quantified by Rényi entropy, when compared with controls (p = 0.002) and idiopathic PD patients (p = 0.0004). Ordinal pattern statistics permitted the identification of LRRK2-associated PD individuals with 93% sensitivity and 93% specificity. Consistent results were found in a subgroup of LRRK2-non-manifesting carriers when compared with controls.ConclusionsIncreased beat-to-beat HRV in LRRK2 G2019S mutation carriers compared with controls and idiopathic PD patients may indicate augmented cardiac autonomic cholinergic activity, suggesting early impairment of central vagal feedback loops in LRRK2-associated PD

Review and classification of variability analysis techniques with clinical applications

Analysis of patterns of variation of time-series, termed variability analysis, represents a rapidly evolving discipline with increasing applications in different fields of science. In medicine and in particular critical care, efforts have focussed on evaluating the clinical utility of variability. However, the growth and complexity of techniques applicable to this field have made interpretation and understanding of variability more challenging. Our objective is to provide an updated review of variability analysis techniques suitable for clinical applications. We review more than 70 variability techniques, providing for each technique a brief description of the underlying theory and assumptions, together with a summary of clinical applications. We propose a revised classification for the domains of variability techniques, which include statistical, geometric, energetic, informational, and invariant. We discuss the process of calculation, often necessitating a mathematical transform of the time-series. Our aims are to summarize a broad literature, promote a shared vocabulary that would improve the exchange of ideas, and the analyses of the results between different studies. We conclude with challenges for the evolving science of variability analysis

Investigation of the relevance of heart rate variability changes after heart transplantation

Heart transplantation has become an established treatment for end-stage heart disease. However, the shortage of donor organs is a major problem and long-term results are limited by allograft rejection. Heart rate variability (HRV) has emerged as a popular noninvasive research tool in cardiology. Analysis of HRV is regarded as a valid technique to assess the sympathovagal balance of the heart. The primary goal of this study was to investigate the relevance of heart rate variability changes after heart transplantation. It was found that spectral analysis of HRV is useful in detecting rejection episodes. Heart transplantation leaves the donor heart denervated. Spectral analysis of HRV was found appropriate to detect functional autonomous reinnervation. Extensive literature review was done to validate the findings. The paper is divided into two parts. The first part of the paper deals mainly with the techniques and current status of heart transplantation. The second part, deals with the relevance of heart rate variability and reinnervation after heart transplantation. The results of the study suggest that heart rate variability analysis is a valuable tool in assessing the cardiovascular status after heart transplantation

Measurement of Autonomic Function in Renal Disease and Diabetes

Renal disease and diabetes lead to dysautonomia resulting in consequences ranging from gastroparesis to sudden death. New technologies to detect dysautonomia, such as 24-hr heart rate variability, are being evaluated and compared to traditional evoked tests. These advances have, however, lead to a lack of standardization in testing batteries, procedures, and reporting formats. This series of 3 studies psychometrically assessed measures of autonomic function (AF) and explored relationships among objective and subjective measures in healthy adults and uremic patients. Participants underwent evoked tests that included change in heart rate with deep breathing and Valsalva. In addition, measures of 24-hr HRV (time-domain: SDNN, SDANN, RMSSD; frequency-domain: total power, low and high frequency) and symptomatology were obtained. Study 1 examined the development and psychometric testing of the Autonomic Symptom Checklist (ASC), an instrument designed to assess autonomic symptomatology, with uremic patients (n=244) and healthy adults (n=34). Findings showed the ASC was able to differentiate among healthy and uremic patients with and without diabetes. Test-retest reliability was moderate to high for most categories. Study 2 established normal, borderline, and abnormal AF values and determined if these values could distinguish healthy (n=158) from uremic adults (n=363). Abnormal values were established at the 2.3 quantile of healthy adults. Uremic patients, especially those with diabetes, had much poorer values than healthy adults. The influence of age and gender on AF measures was attenuated in uremic as compared to healthy adults. Study 3 examined relationships among and the clinical utility of evoked tests, 24- hr HRV, and the ASC. Data were obtained from pre (n=130) and post (n=55) kidney and kidney-pancreas transplant recipients (n=130), and healthy adults (n=22). The frequency of abnormal values was used to identify the most sensitive measure. Measures of 24-hr HRV were more sensitive than evoked measures, with frequency measures being most sensitive. In conclusion, this series of studies established reliability and validity for the ASC, referent values for AF tests, devised a scoring system for AF tests, and found 24-hr HRV measures more sensitive than evoked measures

Linear and nonlinear parameters of heart rate variability in ischemic stroke patients

Introduction Cardiovascular system presents cortical modulation. Post-stroke outcome can be highly influenced by autonomic nervous system disruption. Heart rate variability (HRV) analysis is a simple non-invasive method to assess sympatho-vagal balance. Objectives The purpose of this study was to investigate cardiac autonomic activity in ischemic stroke patients and to asses HRV nonlinear parameters beside linear ones. Methods We analyzed HRV parameters in 15 right and 15 left middle cerebral artery ischemic stroke patients, in rest condition and during challenge (standing and deep breathing). Data were compared with 15 age- and sex-matched healthy controls. Results There was an asymmetric response after autonomic stimulation tests depending on the cortical lateralization in ischemic stroke patients. In resting state, left hemisphere stroke patients presented enhanced parasympathetic control of the heart rate (higher values for RMSSD, pNN50 and HF in normalized units). Right hemisphere ischemic stroke patients displayed a reduced cardiac parasympathetic modulation during deep breathing test. Beside time and frequency domain, using short-term ECG monitoring, cardiac parasympathetic modulation can also be assessed by nonlinear parameter SD1, that presented strong positive correlation with time and frequency domain parameters RMSSD, pNN50, HFnu, while DFA α1 index presented negative correlation with the same indices and positive correlation with the LFnu and LF/HF ratio, indicating a positive association with the sympatho-vagal balance. Conclusions Cardiac monitoring in clinical routine using HRV analysis in order to identify autonomic imbalance may highlight cardiac dysfunctions, thus helping preventing potential cardiovascular complications, especially in right hemisphere ischemic stroke patients with sympathetic hyperactivation

Use of Multiscale Entropy to Characterize Fetal Autonomic Development

The idea that uterine environment and adverse events during fetal development could increase the chances of the diseases in adulthood was first published by David Barker in 1998. Since then, investigators have been employing several methods and methodologies for studying and characterizing the ontological development of the fetus, e.g., fetal movement, growth and cardiac metrics. Even with most recent and developed methods such as fetal magnetocardiography (fMCG), investigators are continuously challenged to study fetal development; the fetus is inaccessible. Finding metrics that realize the full capacity of characterizing fetal ontological development remains a technological challenge. In this thesis, the use and value of multiscale entropy to characterize fetal maturation across third trimester of gestation is studied. Using multiscale entropy obtained from participants of a clinical trial, we show that MSE can characterize increasing complexity due to maturation in the fetus, and can distinguish a growing and developing fetal system from a mature system where loss of irregularity is due to compromised complexity from increasing physiologic load. MSE scales add a nonlinear metric that seems to accurately reflect the ontological development of the fetus and hold promise for future use to investigate the effects of maternal stress, intrauterine growth restriction, or predict risk for sudden infant death syndrome