Patterns of beat-to-beat heart rate variability in advanced heart failure

Inter-day reliability of heart rate complexity and variability metrics in healthy highly active younger and older adults

PURPOSE

To investigate the inter-day reliability of time-domain, frequency-domain, and nonlinear HRV metrics in healthy highly active younger and older adults. The study also assessed the effect of age on the HRV metrics.

METHODS

Forty-four older adults (34 M, 10F; 59 ± 5 years;V ˙ O 2peak  = 40.9 ± 7.6 ml kg-1 min-1) and twenty-two younger adults (16 M, 6F; 22 ± 4 years;V ˙ O 2peak  = 47.2 ± 12.8 ml kg-1 min-1) attended the laboratory. Visit one assessed aerobic fitness through an exercise test. In visits two and three, participants completed a 30-min supine RR interval measurement to derive the HRV metrics.

RESULTS

The younger group (YG) and older group (OG) demonstrated poor to good day-to-day relative and absolute reliability for all HRV metrics (OG, ICCs = 0.33 to 0.69 and between day CVs = 3.8 to 29.2%; YG, ICCs = 0.37 to 0.93 and between day CVs = 3.5 to 36.5%). There was a significant reduction in ApEn (P < 0.001), SampEn (P = 0.031), RMSSD (P < 0.001), SDNN (P < 0.001), LF power (P < 0.001) and HF power (P < 0.001), HRV metrics with ageing. There was no significant effect of age the complexity metrics DFA α1 (P = 0.107), α2 (P = 0.147) and CI-8 (P = 0.493).

CONCLUSION

HRV metrics are reproducible between days in both healthy highly active younger and older adults. There is a decline in linear and nonlinear HRV metrics with age, albeit there being no age-related change in the nonlinear metrics, DFA α1, α2 and CI-8.

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.

Heart rate variability, recovery and stress analysis of an elite rally driver and co-driver during a competition period

To ensure both optimal health and performances, monitoring physiological and psychological states is of main importance for athletes. It is well known that monitoring heart rate variability and using validated questionnaires is useful for monitoring both the health and training status of athletes of different sports. Motorsports such as rally require high levels of physical and mental preparation thus information about psychophysiological status of rally athletes is fundamental. The aim of this study was to assess the autonomic regulation, stress, recovery conditions of one driver and one co-driver competing at the Italian National Rally Championship during their competition period. Heart rate variability parameters, acute recovery and stress states were assessed the day before, during the two days of race and the day following the races. Results showed that driver and co-driver had a sharp decrease of mean RR intervals, root mean square of successive differences between normal heartbeats, and standard deviation of the N-N interval during race days, while the stress index showed the inverse trend, and this behaviour was clearly visible in the Poincaré plots and power spectrum density graphs. The acute recovery and stress states questionnaire showed significant differences in recovery and stress scoring for the driver but not for the co-driver, although the trends were similar. This study describes the psychophysiological demands of a rally competition period suggesting that a daily evaluation of heart rate variability, recovery, stress states is useful for monitoring health status in rally athletes and could be implemented to make decision about training and recovery strategies.

Characterization of autonomic states by complex sympathetic and parasympathetic dynamics

Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises especially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.

Modeling brain-heart interactions from Poincaré plot-derived measures of sympathetic-vagal activity

Recent studies suggest that the interaction between the brain and heart plays a key role in cognitive processes, and measuring these interactions is crucial for understanding the interaction between the central and autonomic nervous systems. However, studying this bidirectional interplay presents methodological challenges, and there is still much room for exploration. This paper presents a new computational method called the Poincaré Sympathetic-Vagal Synthetic Data Generation Model (PSV-SDG) for estimating brain-heart interactions. The PSV-SDG combines EEG and cardiac sympathetic-vagal dynamics to provide time-varying and bidirectional estimators of mutual interplay. The method is grounded in the Poincaré plot, a heart rate variability method to estimate sympathetic-vagal activity that can account for potential non-linearities. This algorithm offers a new approach and computational tool for functional assessment of the interplay between EEG and cardiac sympathetic-vagal activity. The method is implemented in MATLAB under an open-source license. • A new brain-heart interaction modeling approach is proposed. • The modeling is based on coupled synthetic data generators of EEG and heart rate series. • Sympathetic and vagal activities are gathered from Poincaré plot geometry.

Heart Rate Variability Analysis May Identify Individuals With Williams-Beuren Syndrome at Risk of Sudden Death

BACKGROUND

Williams-Beuren syndrome (WBS) (Online Mendelian Inheritance in Man #194050) is a rare genetic multisystem disorder resulting from a chromosomal microdeletion at 7q11.23. The condition is characterized by distinct facies, intellectual disability, and supravalvar aortic stenosis. Those with WBS have an increased risk of sudden death, but mechanisms underlying this phenotype are incompletely understood.

OBJECTIVES

The aim of this study was to quantify and compare autonomic activity as reflected by heart rate variability (HRV) measures in a cohort of individuals with WBS (n = 18) and age- and sex-matched control subjects (n = 18).

METHODS

We performed HRV analysis on 24-hour electrocardiography recordings using nonlinear, time and frequency domain analyses on a cohort of subjects with WBS and age- and sex-matched control subjects enrolled in a prospective cross-sectional study designed to characterize WBS disease natural history.

RESULTS

WBS subjects demonstrated diminished HRV (reflected by the SD of the NN intervals [P = 0.0001], SD of the average NN interval for 5-minute intervals over 24 hours [P < 0.0001], average of the 5-minute SDs of NN intervals for 24 hours [P = 0.0002], root mean square of successive differences of NN intervals [P = 0.0004], short axis of the Poincaré plot (SD1) [P < 0.0001], and long axis of the Poincaré plot [P < 0.0001]) and indirect markers of parasympathetic activity (reflected by the percent of NN intervals different from previous by 50% or more of local average [P < 0.0007], root mean square of successive differences of NN intervals [P = 0.0004], natural log high-frequency power [P = 0.0038], and SD1 [P < 0.0001]). Additional parameters were also significantly different, including natural log very low-frequency power (decreased; P = 0.0002), natural log low-frequency power (decreased; P = 0.0024), and SD1 divided by the long axis of the Poincaré plot (decreased; P < 0.0001).

CONCLUSIONS

Individuals with WBS demonstrate significant HRV abnormalities consistent with diminished autonomic reserve. Future studies will be needed to determine the relationship between autonomic dysregulation observed and sudden death risk seen in these patients. (Impact of Elastin Mediated Vascular Stiffness on End Organs; NCT02840448).

An assessment of heart rate and blood pressure asymmetry in the diagnosis of vasovagal syncope in females

Introduction: Heart Rate Asymmetry (HRA) describes a phenomenon of differences between accelerations and decelerations in human heart rate. Methods used for HRA assessment can be further implemented in the evaluation of asymmetry in blood pressure variations (Blood Pressure Asymmetry-BPA). Methods: We have analyzed retrospectively the series of heartbeat intervals extracted from ECG and beat-to-beat blood pressure signals from 16 vasovagal patients (age: 32.1 ± 13.3; BMI: 21.6 ± 3.8; all female) and 19 healthy subjects (age: 34.6 ± 7.6; BMI: 22.1 ± 3.4; all female) who have undergone tilt test (70°). Asymmetry was evaluated with Poincaré plot-based methods for 5 min recordings from supine and tilt stages of the test. The analyzed biosignals were heart rate (RR), diastolic (dBP) and systolic Blood Pressure (sBP) and Pulse Pressure (PP). In the paper we explored the differences between healthy and vasovagal women. Results: The changes of HRA indicators between supine and tilt were observed only in the control group (Porta Index p = 0.026 and Guzik Index p = 0.005). No significant differences in beat-to-beat variability (i.e. spread of points across the line of identity in Poincaré plot-SD1) of dBP was noted between supine and tilt in the vasovagal group (p = 0.433 in comparison to p = 0.014 in healthy females). Moreover, in vasovagal patients the PP was significantly different (supine: 41.47; tilt: 39.27 mmHg) comparing to healthy subjects (supine: 35.87; tilt: 33.50 mmHg) in supine (p = 0.019) and in tilt (p = 0.014). Discussion: Analysis of HRA and BPA represents a promising method for the evaluation of cardiovascular response to orthostatic stressors, however currently it is difficult to determine a subject's underlying health condition based only on these parameters.

Six-year longitudinal prospective comparative study between preoperative and postoperative heart rate variability indices in congenital craniovertebral junction anomalies

Background

Craniovertebral junction (CVJ) anomalies involve mosaic interaction of multifaceted neurovascular and bony elements. Most of them present late in the course of illness usually as acute presentations following trivial trauma. Knowing subclinical autonomic dysfunction in such anomalies when managed medically can not only indicate progression but also provide en route to early intervention for better outcomes, especially in relatively asymptomatic patients.

Materials and Methods

We conducted a 6-year longitudinal prospective study including 40 consecutive patients of CVJ anomalies with clinical, radiological, and heart rate variability (HRV) parameters and found their correlation in preoperative and follow-up period.

Results

Twenty-eight patients were male and the rest were female. The mean age was 32 years with the least age being 8 years and maximum age being 75 years old. Mean Nurick's grade and Barthel's index were 1.8 and 83.75, respectively. 38% had severe-to-moderate compression. The mean follow-up was 17.4 months. Both sympathetic and parasympathetic oscillator HRV indices were significantly affected in the preoperative period (P ≤ 0.001) with no association with Nurick's grade or degree of compression although there was association with grade of Barthel's index. Poincare plots showed "fan," "complex," or "torpedo" patterns in 36 patients. Forty patients had both preoperative and follow-up clinical grade whereas 22 patients HRV tests in the above periods. None of the HRV indices showed significant improvement at follow-up. Nonetheless both sympathetic and parasympathetic did improve at follow-up with sympathetic tone registering better scores. Poincare plots showed improvement toward "comet" patterns in all patients.

Conclusion

HRV indices not only help in prognosticating but may also help in predicting outcomes.

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process.

Influenza virus replication in cardiomyocytes drives heart dysfunction and fibrosis

Cardiac dysfunction is a common complication of severe influenza virus infection, but whether this occurs due to direct infection of cardiac tissue or indirectly through systemic lung inflammation remains unclear. To test the etiology of this aspect of influenza disease, we generated a novel recombinant heart-attenuated influenza virus via genome incorporation of target sequences for miRNAs expressed in cardiomyocytes. Compared with control virus, mice infected with miR-targeted virus had significantly reduced heart viral titers, confirming cardiac attenuation of viral replication. However, this virus was fully replicative in the lungs and induced similar systemic inflammation and weight loss compared to control virus. The miR-targeted virus induced fewer cardiac conduction irregularities and significantly less fibrosis in mice lacking interferon-induced transmembrane protein 3 (IFITM3), which serve as a model for influenza-associated cardiac pathology. We conclude that robust virus replication in the heart is required for pathology, even when lung inflammation is severe.

Chaotic and stochastic dynamics of epileptiform-like activities in sclerotic hippocampus resected from patients with pharmacoresistant epilepsy

The types of epileptiform activity occurring in the sclerotic hippocampus with highest incidence are interictal-like events (II) and periodic ictal spiking (PIS). These activities are classified according to their event rates, but it is still unclear if these rate differences are consequences of underlying physiological mechanisms. Identifying new and more specific information related to these two activities may bring insights to a better understanding about the epileptogenic process and new diagnosis. We applied Poincaré map analysis and Recurrence Quantification Analysis (RQA) onto 35 in vitro electrophysiological signals recorded from slices of 12 hippocampal tissues surgically resected from patients with pharmacoresistant temporal lobe epilepsy. These analyzes showed that the II activity is related to chaotic dynamics, whereas the PIS activity is related to deterministic periodic dynamics. Additionally, it indicates that their different rates are consequence of different endogenous dynamics. Finally, by using two computational models we were able to simulate the transition between II and PIS activities. The RQA was applied to different periods of these simulations to compare the recurrences between artificial and real signals, showing that different ranges of regularity-chaoticity can be directly associated with the generation of PIS and II activities.

Temporal lobe epilepsy (TLE) is the most prevalent type of epilepsy in adults and hippocampal sclerosis is the major pathophysiological substrate of pharmaco-refractory TLE. Different patterns of epileptiform-like activity have been described in human hippocampal sclerosis, but the standard analysis applied to characterize the activities usually do not consider the nonlinear features that epileptiform patterns exhibit. Here, using Poincaré map and Recurrence Quantitative Analysis we characterized the most prevalent type of epileptiform-like activities—interictal-like events (II) and periodic ictal spiking (PIS), recorded in vitro from resected hippocampi of pharmacoresistant patients with TLE—according to their levels of stochasticity, chaoticity and determinism. The II activities showed to be more chaotic with complex rhythmicity than PIS activities. The nonlinear dynamic differences between II and PIS leads us to conjecture that they are expressions of different seizure susceptibility. We also identified that each hippocampal subfield expresses II and PIS activities in a specific and different way. Finally, from the modulation of internal parameters of two computational models, we show the conversion of one type of activity into the other, showing how specific neuron networks synchronize over time, leading to II and PIS activities and then into a generalized seizure.

Study on the nonfatigue and fatigue states of orchard workers based on electrocardiogram signal analysis

In recent years, fatigue has become an important issue in modern life that cannot be ignored, especially in some special occupations. Agricultural workers are high-risk occupations that, under fatigue conditions over a long period, will cause health problems. In China, since very few studies have focused on the fatigue state of agricultural workers, we were interested in using electrocardiogram (ECG) signals to analyze the fatigue state of agricultural workers. Healthy agricultural workers were randomly recruited from hilly orchards in South China. Through the field experiment, 130 groups of 5-min interval ECG signals were collected, and we analyzed the ECG signal by HRV. The time domain (meanHR, meanRR, SDNN, RMSSD, SDSD, PNN20, PNN50 and CV), frequency domain (VLF percent, LF percent, HF percent, LF norm, HF norm and LF/HF) and nonlinear parameters (SD1, SD2, SD1/SD2 and sample entropy) were calculated and Spearman correlation coefficient analysis and Mann-Whitney U tests were performed on each parameter for further analysis. For all subjects, nine parameters were slightly correlated in nonfatigue and fatigue state. Six parameters were significantly increased and ten HRV parameters were significantly decreased compared the nonfatigue state. As for males, fifteen parameters were significantly different, and for females, eighteen parameters were significantly different. In addition, the probability density functions of SDNN, SDSD, VLF%, HFnorm and LF/HF were significantly different in nonfatigue and fatigue state for different genders, and the nonlinear parameters become more discrete compared the nonfatigue state. Finally, we obtained the most suitable parameters, which reflect the fatigue characteristics of orchard workers under different genders. The results have instructional significance for identifying fatigue in orchard workers and provide a convincing and valid reference for clinical diagnosis.

Bidimensional and Tridimensional Poincaré Maps in Cardiology: A Multiclass Machine Learning Study

Heart rate is a nonstationary signal and its variation may contain indicators of current disease or warnings about impending cardiac diseases. Hence, heart rate variation analysis has become a noninvasive tool to further study the activities of the autonomic nervous system. In this scenario, the Poincaré plot analysis has proven to be a valuable tool to support cardiac diseases diagnosis. The study’s aim is a preliminary exploration of the feasibility of machine learning to classify subjects belonging to five cardiac states (healthy, hypertension, myocardial infarction, congestive heart failure and heart transplanted) using ten unconventional quantitative parameters extracted from bidimensional and three-dimensional Poincaré maps. Knime Analytic Platform was used to implement several machine learning algorithms: Gradient Boosting, Adaptive Boosting, k-Nearest Neighbor and Naïve Bayes. Accuracy, sensitivity and specificity were computed to assess the performances of the predictive models using the leave-one-out cross-validation. The Synthetic Minority Oversampling technique was previously performed for data augmentation considering the small size of the dataset and the number of features. A feature importance, ranked on the basis of the Information Gain values, was computed. Preliminarily, a univariate statistical analysis was performed through one-way Kruskal Wallis plus post-hoc for all the features. Machine learning analysis achieved interesting results in terms of evaluation metrics, such as demonstrated by Adaptive Boosting and k-Nearest Neighbor (accuracies greater than 90%). Gradient Boosting and k-Nearest Neighbor reached even 100% score in sensitivity and specificity, respectively. The most important features according to information gain are in line with the results obtained from the statistical analysis confirming their predictive power. The study shows the proposed combination of unconventional features extracted from Poincaré maps and well-known machine learning algorithms represents a valuable approach to automatically classify patients with different cardiac diseases. Future investigations on enriched datasets will further confirm the potential application of this methodology in diagnostic.

Cardiac and Autonomic Dysfunctions Assessed Through Recurrence Quantitative Analysis of Electrocardiogram Signals and an Application to the 6-Hydroxydopamine Parkinson's Disease Animal Model

A classic method to evaluate autonomic dysfunction is through the evaluation of heart rate variability (HRV). HRV provides a series of coefficients, such as Standard Deviation of n-n intervals (SDNN) and Root Mean Square of Successive Differences (RMSSD), which have well-established physiological associations. However, using only electrocardiogram (ECG) signals, it is difficult to identify proper autonomic activity, and the standard techniques are not sensitive and robust enough to distinguish pure autonomic modulation in heart dynamics from cardiac dysfunctions. In this proof-of-concept study we propose the use of Poincaré mapping and Recurrence Quantification Analysis (RQA) to identify and characterize stochasticity and chaoticity dynamics in ECG recordings. By applying these non-linear techniques in the ECG signals recorded from a set of Parkinson’s disease (PD) animal model 6-hydroxydopamine (6-OHDA), we showed that they present less variability in long time epochs and more stochasticity in short-time epochs, in their autonomic dynamics, when compared with those of the sham group. These results suggest that PD animal models present more “rigid heart rate” associated with “trembling ECG” and bradycardia, which are direct expressions of Parkinsonian symptoms. We also compared the RQA factors calculated from the ECG of animal models using four computational ECG signals under different noise and autonomic modulatory conditions, emulating the main ECG features of atrial fibrillation and QT-long syndrome.

Methodology for the prediction of paroxysmal atrial fibrillation based on heart rate variability feature analysis

Atrial fibrillation (AF) is the most clinically diagnosed arrhythmia, as its prevalence increases with age, and its initial stage is paroxysmal atrial fibrillation (PAF). This pathology usually triggers hemodynamic disorders that can generate cerebrovascular accidents (CVA), causing morbidity and even death. The aim of this study is to predict the occurrence of PAF episodes in order to take precautions to prevent PAF episodes. The PhysioNet AFPDB prediction database was used to extract 77 heart rate variability (HRV) features using time domain, geometrical analysis, Poincaré plot, nonlinear analysis, detrended fluctuation analysis, autoregressive modeling, fast Fourier transform (FFT), Lomb-Scargle periodogram, wavelet packet transform (WPT) and bispectrum measurements. The number of features was reduced using the near-zero value, correlation, and recursive feature elimination (RFE) methods for time windows of 1, 2, 5, 10, and 30 min. Feature selection was performed using backwards selection, genetic algorithm, analysis of variance (ANOVA), and non-dominated sorting genetic algorithm (NSGA-III) methods, and then random forest, conditional random forest, k-nearest neighbor (KNN), and support vector machine (SVM) classification algorithms were applied and evaluated using 10-fold cross-validation. The proposed method achieved a precision of 93.24% with a 5-minute window and 89.21% with a 2-minute window, improving performance in predicting PAF when compared with similar studies in the literature.

Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes

Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF). Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. In this paper, we explored the possibility to feed machine-learning (ML) algorithms using unconventional quantitative parameters extracted from Poincaré plots (generated from 24-h electrocardiogram recordings) to classify patients with CHF belonging to different New York Heart Association (NYHA) classes. We performed in sequence the following investigations: first, a statistical analysis was carried out on 9 morphological parameters, automatically measured from Poincaré plots. Subsequently, a feature selection through a wrapper with a 10-fold cross-validation method was performed to find the best subset of features which maximized the classification accuracy for each considered ML algorithm. Finally, patient classification was assessed through a ML analysis using AdaBoost of Decision Tree, k-Nearest Neighbors and Naive Bayes algorithms. A univariate statistical analysis proved 5 out of 9 parameters presented statistically significant differences among patients of distinct NYHA classes; similarly, a multivariate logistic regression confirmed the importance of the parameter ρy in the separability between low-risk and high-risk classes. The ML analysis achieved promising results in terms of evaluation metrics (especially the Naive Bayes algorithm), with accuracies greater than 80% and Area Under the Receiver Operating Curve indices greater than 0.7 for the overall three algorithms. The study indicates the proposed features have a predictive power to discriminate the NYHA classes, to which the features seem evenly correlated. Despite the NYHA classification being subjective and easily recognized by cardiologists, the potential relevance in the clinical cardiology of the proposed features and the promising ML results implies the methodology could be a valuable approach to automatically classify CHF. Future investigations on enriched datasets may further confirm the presented evidence.

Asymmetric multiscale multifractal analysis (AMMA) of heart rate variability

Objective.The physiological activity of the heart is controlled and modulated mostly by the parasympathetic and sympathetic nervous systems. Heart rate variability (HRV) analysis is therefore used to observe fluctuations that reflect changes in the activity in these two branches. Knowing that acceleration and deceleration patterns in heart rate fluctuations are asymmetrically distributed, the ability to analyze HRV asymmetry was introduced into MMA.Approach. The new method is called asymmetric multiscale multifractal analysis (AMMA) and the analysis involved six groups: 36 healthy persons, 103 cases with aortic valve stenosis, 36 with hypertrophic cardiomyopathy, 32 with atrial fibrillation, 59 patients with coronary artery disease (CAD) and 13 with congestive heart failure.Main results. Analyzing the results obtained for the 6 groups of patients based on the AMMA method, i.e. comparing the Hurst surfaces for heart rate decelerations and accelerations, it was noticed that these surfaces differ significantly. And the differences occur in most groups for large fluctuations (multifractal parameterq > 0). In addition, a similarity was found for all groups for the AMMA Hurst surface for decelerations to the MMA Hurst surface-heart rate decelerations (lengthening of the RR intervals) appears to be the main factor determining the shape of the complete Hurst surface and so the multifractal properties of HRV. The differences between the groups, especially for CAD, hypertrophic cardiomyopathy and aortic valve stenosis, are more visible if the Hurst surfaces are analyzed separately for accelerations and decelerations.Significance. The AMMA results presented here may provide additional input for HRV analysis and create a new paradigm for future medical screening. Note that the HRV analysis using MMA (without distinguishing accelerations from decelerations) gave satisfactory screening statistics in our previous studies.

A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool

We propose a novel machine learning-based method for analysing multi-night actigraphy signals to objectively classify and differentiate nocturnal awakenings in individuals with chronic insomnia (CI) and their cohabiting healthy partners. We analysed nocturnal actigraphy signals from 40 cohabiting couples with one partner seeking treatment for insomnia. We extracted 12 time-domain dynamic and nonlinear features from the actigraphy signals to classify nocturnal awakenings in healthy individuals and those with CI. These features were then used to train two machine learning classifiers, random forest (RF) and support vector machine (SVM). An optimization algorithm that incorporated the predicted quality of each night for each individual was used to classify individuals into CI or healthy sleepers. Using the proposed actigraphic signal analysis technique, coupled with a rigorous leave-one-out validation approach, we achieved a classification accuracy of 80% (sensitivity: 76%, specificity: 82%) in classifying CI individuals and their healthy bed partners. The RF classifier (accuracy: 80%) showed a better performance than SVM (accuracy: 75%). Our approach to analysing the multi-night nocturnal actigraphy recordings provides a new method for screening individuals with CI, using wrist-actigraphy devices, facilitating home monitoring.

Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial

The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users' understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2.

Lorenz Plot Analysis in Dogs with Sinus Rhythm and Tachyarrhythmias

Simple Summary

The Lorenz plot (LP) is a geometrical method to assess the dynamics of heart rate variability. It consists of a two-dimensional Cartesian coordinate system derived from electrocardiographic monitoring, in which each recorded R-R interval is plotted as a function of the previous R-R interval, and the values of each pair of successive R-R interval define a dot in the plot. The resultant clusters of dots can be evaluated quantitatively and qualitatively, and categorized into distinct geometrical patterns. In humans, several studies have demonstrated that the analysis of LP patterns (LPPs) has the potential to speed-up and improve the accuracy of arrhythmia detection and differentiation, especially in patients with tachyarrhythmias. As data on LP analysis are limited in dogs, this study describes the graphic features of LP derived from Holter recordings obtained in dogs with sinus rhythm and tachyarrhythmias, and analyzes the usefulness of LPP recognition in this species. We sought to evaluate if distinct cardiac rhythms imprint distinct and reproducible LPPs in dogs, as previously described in humans, and if each LLP can be used as a sensitive and specific indicator of a particular cardiac rhythm in this species.

Abstract

The Lorenz plot (LP), a graphical representation of heart rate variability, has been poorly studied in dogs to date. The present study aimed to describe the graphic features of LP in dogs with sinus rhythm (SR) and tachyarrhythmias, and to analyze the usefulness of its pattern recognition. One hundred and nineteen canine Holter recordings were retrospectively evaluated. Cardiac rhythms were classified as: SR; SR with frequent (>100) premature ectopies (atrial, SR-APCs; ventricular, SR-VPCs; atrial and ventricular, SR-APCs-VPCs); atrial fibrillation (AF); and AF with frequent VPCs (AF-VPCs). Lorenz plots were studied qualitatively and quantitatively, and classified by distinct LP patterns (LPPs). Repeatability and reproducibility of LPP classification and diagnostic value were determined. Recordings included: 48 SR, 9 SR-APCs, 35 SR-VPCs, 5 SR-APCs-VPCs, 4 AF, and 18 AF-VPCs. Ten LPPs were identified: comet (n = 12), torpedo (n = 3), Y-shaped (n = 6), diamond (n = 10), diamond with a central silent zone (n = 17), double side-lobe (DSL) (n = 47), triple side-lobe (n = 1), quadruple side-lobe (n = 2), fan (n = 18), and fan with DSL (n = 3). Repeatability and reproducibility of LPP classification were excellent. The DSL pattern was both highly sensitive (91.3%) and specific (94.5%) for SR with frequent premature ectopies, either APCs, or VPCs, or both. The remaining LPPs had lower diagnostic value (high specificity but low sensitivity). Distinct rhythms imprint distinct and reproducible LPPs in dogs. The majority of canine LPPs are specific but insensitive indicators of SR and tachyarrhythmias.

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

BACKGROUND

We developed CEPS as an open access MATLAB^® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements.

METHODS

Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system.

RESULTS

The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing.

CONCLUSIONS

We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.

Cardiac Autonomic Dysfunction and Incidence of de novo Atrial Fibrillation: Heart Rate Variability vs. Heart Rate Complexity

BACKGROUND

The REACT DX registry evaluates standard therapies to episodes of long-lasting atrial tachyarrhythmias and assesses the quality of sensing and stability of the lead and the implantable cardioverter-defibrillator (ICD) (BIOTRONIK Lumax VR-T DX and successors) over at least a 1-year follow-up period.

OBJECTIVE

To study the association between the risk of de novo device-detected atrial fibrillation (AF), the autonomic perturbations before the onset of paroxysmal AF and a 7-days heart rate variability (7dHRV) 1 month after ICD implantation.

METHODS

The registry consists of 234 patients implanted with an ICD, including 10 with de novo long-lasting atrial tachyarrhythmias with no prior history of AF. The patients were matched via the propensity-score methodology as well as for properties directly influencing the ECGs recorded using GE CardioMem CM 3000. Heart rate variability (HRV) analysis was performed using standard parameters from time- and frequency-domains, and from non-linear dynamics.

RESULTS

No linear HRV was associated with an increased risk of AF (p = n.s.). The only significant approach was derived from symbolic dynamics with the parameter "forbidden words" which distinguished both groups on all 7 days of measurements (p < 0.05), thereby quantifying the heart rate complexity (HRC) as drastically lower in the de novo AF group.

CONCLUSION

Cardiac autonomic dysfunction denoted by low HRC may be associated with higher AF incidence. For patients with mild to moderate heart failure, standard HRV parameters are not appropriate to quantify cardiac autonomic perturbations before the onset of AF. Further studies are needed to determine the individual risk for AF that would enable interventions to restore autonomic balance in the general population.

Heart rate variability analysis in horses for the diagnosis of arrhythmias

Heart rate variability (HRV) analysis has been performed on ECG-derived data sets for more than 170 years but is currently undergoing a rapid evolution, thanks to the expansion of the human and veterinary medical technology sector. Traditional HRV analysis was initially performed to identify changes in vago-sympathetic balance, while the most recent focus has expanded to include the use of complex computer algorithms, neural networks and machine learning technology to identify cardiac arrhythmias, particularly atrial fibrillation (AF). Some of these techniques have recently been translated for use in the field of equine cardiology, with particular focus on improving the diagnosis of arrhythmias both at rest and during exercise. This review focuses on understanding the basic HRV variables and important factors to consider when collecting data for use in HRV analysis. In addition, the use of HRV analysis for the diagnosis of arrhythmias is discussed from human, small animal and equine perspectives. Finally, the future of HRV analysis is briefly introduced, including an overview of future developments in this rapidly expanding and exciting field.

Assessment of autonomous nerve system through non-linear heart rate variability outcomes in sedentary healthy adults

Background

Heart rate variability (HRV) is a psycho-physiological phenomenon with broad health implications. Different data analysis methods have been used to assess the autonomic nervous system activity, but the validation of new indexes that accurately describe its balance through non-invasive methods (i.e., HRV analysis) is of clinical interest. This study aimed: (i) to evaluate the association of the Stress Score (SS) and the Sympathetic/Parasympathetic Ratio (S/PS) with time domain and frequency domain analysis of HRV, and (ii) to set reference values of SS and S/PS in sedentary healthy adults.

Methods

A total of 156 sedentary healthy adults (38.4 ± 15.57 years old, 81 women), aged were involved in this study. HRV was measured for 15 min in a supine position at rest. SS and S/PS were calculated from the non-linear HRV analyses based on Poincare Plot.

Results

Stress Score showed a non-linear negative power-law relationship with SDNN (β = -0.969; R ² = 0.963; P < 0.001), RMSSD (β = -0.867; R ² = 0.722; P < 0.001), high frequency (β = -0.834; R ² = 0.752; P =< 0.001), low frequency (β = -0.627; R ² = 0.330; P < 0.001), SD1 (β = -0.867; R ² = 0.722; P < 0.001) and SD2 (β = -1.000; R ² > 0.999; P < 0.001). There was observed a negative cubic relationship between SS with PNN50 (β = -1.972; R ² = 0.644; P < 0.001). A linear regression model was conducted between SS with Ratio Low/High Frequency (β = 0.026; R ² < 0.001; P = 0.750). Non-linear power-law regression models were built between S/PS and SDNN (β = -0.990; R ² = 0.981; P < 0.001), RMSSD (β = -0.973; R ² = 0.939; P < 0.001), high frequency (β = -0.928; R ² = 0.970; P < 0.001), low frequency (β = -2.344; R ² = 0.557; P < 0.001), SD1 (β = -0.973; R ² = 0.939; P < 0.001) and SD2 (β = -0.611; R ² = 0.908; P < 0.001). A non-linear negative regression model was built between S/PS and PNN50 (β = -3.412; R ² = 0.868; P < 0.001). A linear regression model was conducted between S/PS and SD2/SD1 (β = 0.075; R ² = 0.006; P < 0.001).

Conclusion

Our results support the use of SS as a sympathetic activity marker, and S/PS as an indicator of the sympathetic and parasympathetic activity of the autonomic nervous system in sedentary healthy adults.

Linear and nonlinear analyses of normal and fatigue heart rate variability signals for miners in high-altitude and cold areas

BACKGROUND AND OBJECTIVE

Fatigue is an important cause of operational errors, and human errors are the main cause of accidents. This study is an exploratory study in China. Field tests were conducted on heart rate variability (HRV) parameters and physiological indicators of fatigue among miners in high-altitude, cold and low-oxygen areas. This paper studies heart activity patterns during work fatigue in miners.

METHODS

Fatigue affects both the sympathetic and parasympathetic nervous systems, and it is expressed as an abnormal pattern of HRV parameters. Thirty miners were selected as subjects for a field test, and HRV was extracted from 60 groups of electrocardiography (ECG) datasets as basic signals for fatigue analysis. Then, we analyzed the HRV signals of the miners using linear (time domain and frequency domain) and nonlinear dynamics (Poincaré plot and sample entropy (SampEn)), and a Pearson's correlation coefficient analysis and t-tests were performed on the measured indices.

RESULTS

The results showed that the time-domain indices (SDNN, RMSSD, SDSD, pNN50, RRn, heart rate (HR), R-wave humps (RH)) and the coefficient of variation (CV)) and the frequency-domain indices (low frequency/high frequency (LF/HF), LFnorm and HFnorm) clearly changed after fatigue. These features were selected using a Poincaré plot, sample entropy, Pearson's correlation coefficient and a t-test for further analysis. The fatigue characteristics and sensitivity parameters of miners in a high-altitude, cold and hypoxic environment were obtained.

CONCLUSIONS

This study provides deep insight into the use of linear and nonlinear fatigue characteristics to effectively and reliably identify miner fatigue. Furthermore, the study provides a reference for clinical studies of acute mountain sickness in high-altitude, cold and hypoxic environments.

Comparative study of ANN and fuzzy classifier for forecasting electrical activity of heart to diagnose Covid-19

Covid-19 is a dangerous communicable virus which lets down the world economy. Severe respiratory syndrome SARS-COV-2 leads to Corona Virus Disease (COVID-19) and has the capability of transmission through human-to-human and surface-to-human transmission leads the world to catastrophic phase. Computational system based biological signal analysis helps medical officers in handling COVID-19 tasks like ECG monitoring at Intensive care, fatal ventricular fibrillation, etc., This paper is on diagnosing heart dysfunctions such as tachycardia, bradycardia, ventricular fibrillation, cardiac arrhythmia using fuzzy relations and artificial intelligence algorithm. In this study, the heart pulse base signal and features like spectral entropy, largest lyapunov exponent, Poincare plot and detrended fluctuation analysis are extracted and presented for classification purpose. The RR intervals of Poincare plot summarize RR time series obtained from an ECG in one picture, and a time interval quantities derives information duration of HRV. This analysis eases the prediction of heart rate fluctuation due to Covid or other heart disorders. The better accuracy level in diagnosing heart pulse irregularity using Artificial Neural network(ANN) is an integer value (0 to 4)but for Fuzzy Classifier, it is 0.8 to 0.9.The processing time for analyzing heart dysfunctionalties is 0.05 s using ANN which is far better than Fuzzy classifier.

Detection of subjects with ischemic heart disease by using machine learning technique based on heart rate total variability parameters

OBJECTIVE

Ischemic heart disease (IHD), in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. The clinical assessment is based on typical symptoms and finally confirmed, invasively, by coronary angiography. Recently, heart rate variability (HRV) analysis as well as some machine learning algorithms like Artificial Neural Networks (ANNs) were used to identify cardiovascular arrhythmias and, only in few cases, to classify IHD segments in a limited number of subjects. The goal of this study was the identification of the ANN structure and the HRV parameters producing the best performance to identify IHD patients in a non-invasive way, validating the results on a large sample of subjects. Moreover, we examined the influence of a clinical non-invasive parameter, the left ventricular ejection fraction (LVEF), on the classification performance.

APPROACH

To this aim, we extracted several linear and non-linear parameters from 24h RR signal, considering both normal and ectopic beats (Heart Rate Total Variability), of 251 normal and 245 IHD subjects, matched by age and gender. ANNs using several different combinations of these parameters together with age and gender were tested. For each ANN, we varied the number of hidden neurons from 2 to 7 and simulated 100 times changing randomly training and test dataset.

MAIN RESULTS

The HRTV parameters showed significant greater variability in IHD than in normal subjects. The ANN applied to meanRR, LF, LF/HF, Beta exponent, SD2 together with age and gender reached a maximum accuracy of 71.8% and, by adding as input LVEF, an accuracy of 79.8%.

SIGNIFICANCE

The study provides a deep insight into how a combination of some HRTV parameters and LVEF could be exploited to reliably detect the presence of subjects affected by IHD.

Adult zebrafish ventricular electrical gradients as tissue mechanisms of ECG patterns under baseline vs. oxidative stress

AIMS

In mammalian ventricles, electrical gradients establish electrical heterogeneities as essential tissue mechanisms to optimize mechanical efficiency and safeguard electrical stability. Electrical gradients shape mammalian electrocardiographic patterns; disturbance of electrical gradients is proarrhythmic. The zebrafish heart is a popular surrogate model for human cardiac electrophysiology thanks to its remarkable recapitulation of human electrocardiogram and ventricular action potential features. Yet, zebrafish ventricular electrical gradients are largely unexplored. The goal of this study is to define the zebrafish ventricular electrical gradients that shape the QRS complex and T wave patterns at baseline and under oxidative stress.

METHODS AND RESULTS

We performed in vivo electrocardiography and ex vivo voltage-sensitive fluorescent epicardial and transmural optical mapping of adult zebrafish hearts at baseline and during acute H2O2 exposure. At baseline, apicobasal activation and basoapical repolarization gradients accounted for the polarity concordance between the QRS complex and T wave. During H2O2 exposure, differential regional impairment of activation and repolarization at the apex and base disrupted prior to baseline electrical gradients, resulting in either reversal or loss of polarity concordance between the QRS complex and T wave. KN-93, a specific calcium/calmodulin-dependent protein kinase II inhibitor (CaMKII), protected zebrafish hearts from H2O2 disruption of electrical gradients. The protection was complete if administered prior to oxidative stress exposure.

CONCLUSIONS

Despite remarkable apparent similarities, zebrafish and human ventricular electrocardiographic patterns are mirror images supported by opposite electrical gradients. Like mammalian ventricles, zebrafish ventricles are also susceptible to H2O2 proarrhythmic perturbation via CaMKII activation. Our findings suggest that the adult zebrafish heart may constitute a clinically relevant model to investigate ventricular arrhythmias induced by oxidative stress. However, the fundamental ventricular activation and repolarization differences between the two species that we demonstrated in this study highlight the potential limitations when extrapolating results from zebrafish experiments to human cardiac electrophysiology, arrhythmias, and drug toxicities.

Quantifying the lagged Poincaré plot geometry of ultrashort heart rate variability series: automatic recognition of odor hedonic tone

The application of Poincaré plot analysis to characterize inter-beat interval dynamics has been successfully proposed in the scientific literature for the assessment of humans' physiological states and related aberrations. In this study, we proposed novel descriptors to trace the evolution of Poincaré plot shape over the lags. Their reliability in ultra-short cardiovascular series analysis was validated on synthetic inter-beat series generated through a physiologically plausible integral pulse frequency modulation model. Furthermore, we used the proposed approach for the investigation of the direct relationship between autonomic nervous system (ANS) dynamics and hedonic olfactory elicitation, in a group of 30 healthy subjects. Participants with a similar olfactory threshold were selected, and were asked to score 5-s stimuli in terms of arousal and valence levels according to the Russell's circumflex model of affect. Their ANS response was investigated in 35-s windows after the elicitation. Experimental results showed a gender-specific, high discriminant power of the proposed approach, discerning between pleasant and unpleasant odorants with an accuracy of 83.33% and 73.33% for men and for women, respectively. Graphical Abstract Olfaction plays a crucial role in our life and is strictly related to the Autonomic Nervous System (ANS) activity, which can be monitored studying Heart Rate Variability. We used the Lagged Poincare Plot approach to recognize gender-specific ANS response in 35-second windows after the elicitation through pleasant/unpleasant odorants.

Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Geometry of the Poincaré plot can segregate the two arms of autonomic nervous system - A hypothesis

Autonomic Nervous System (ANS) operates to achieve the optimum physiological functioning and maintains homeostasis in a tonic and continuous manner. Evaluation of ANS profile is crucial in assessing autonomic dysfunction. Conventional ANS evaluation procedures fail to capture minute dynamic alterations of ANS activities. The ANS output pattern is appropriately reflected in the fine alteration of the resting heart rate (HR). HR is a non-stationary variable, results from the dynamic interplay between the multiple physiologic mechanisms. The control of cardiac rate or the chronotropic regulation of heart is considered as a coupled network of oscillators, each representing a specific facet of the cardiovascular reflex. The slower vasomotor oscillation via sympathetic system is combined with rapid respiratory oscillation by parasympathetic system to modulate the intrinsic oscillation pattern of the SA Node. Heart Rate Variability (HRV) is used to understand the autonomic influence on cardiovascular system in health and disease. Fourier decomposition of HRV offers us mainly two different frequency components. High frequency (HF) variation indicates parasympathetic variability due to respiration and Low frequency (LF) mainly implicates tonic sympathetic influence, due to slower vasomotor modulation of heart rate. However, different studies show conflicting results and direct recording of sympathetic nerve activity also failed to correlate with LF power in either healthy subjects or in patients with increased cardiac sympathetic drive. A scatter-plot where each R-R interval is plotted against the preceding R-R interval forms a distributed elliptic point cloud in two dimensional plane. The phase space realization of this plot with dimension two and delay one is referred to as Poincaré plot analysis, an emerging quantitative-visual technique where the shape of the plot is categorized into different functional classes. The plot provides summary as well as detailed beat-to-beat information of the heart. This plot can be extended to three dimensions and with multi-lag, offering more insight and information. A mathematical expression was developed by an interventional study by Toichi et al., using pharmacological blockers during different physiological variables that calculated the lengths of transverse and longitudinal axes of the Poincaré plot to derive two quantitative expressions of sympathetic and vagal influence on HRV: 'cardiac sympathetic index' (CSI)) and 'cardiac vagal index' (CVI). In the present study, we emulate Poincaré plot patterns seen in normal range of sympatho-vagal balances and also in Diabetes Mellitus (DM), known to cause autonomic dysfunction. The emerging pattern of R-R interval time series would provide valuable insight into the altered temporal dynamics and also extract crucial features embedded within. DM is a major public health crisis globally and particularly in Indian population. We hypothesize that, CSI and CVI will effectively segregate the two arms of ANS and can be utilized as an effective evaluation tool to explore the disease status in patients of Diabetes Mellitus. We also propose that, the dynamics of fluctuations in physiological rhythms that exhibit long-term correlation and memory, can also be explored and expressed quantitatively by incorporating various degrees of 'lag' in these recurrence plots.

Brain and Body Emotional Responses: Multimodal Approximation for Valence Classification

In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their operation and the relationship between them remains unknown. In this context, in the present work, we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence. To this end, the electroencephalography (EEG) signal, electrocardiography (ECG) signal and skin temperature of 24 subjects have been studied. Each methodology has been evaluated individually, finding characteristic patterns of positive and negative emotions in each of them. After feature selection of each methodology, the results of the classification showed that, although the classification of emotions is possible at both central and peripheral levels, the multimodal approach did not improve the results obtained through the EEG alone. In addition, differences have been observed between cerebral and peripheral responses in the processing of emotions by separating the sample by sex; though, the differences between men and women were only notable at the peripheral nervous system level.

The Effect of Antiarrhythmic Drugs on the Beat Rate Variability of Human Embryonic and Human Induced Pluripotent Stem Cell Derived Cardiomyocytes

Embryonic stem cell (ESC) derived tissue is a promising tool to be used in different clinical, preclinical and also scientific settings, for example as in vivo biological pacemaker, preclinical drug safety screening tool or ultimately as part of a cell replacement therapy. However, before ESC derived tissue can be used routinely for these purposes in humans, further studies are needed. In this context, the aims of the present study were to examine the effect of antiarrhythmic drugs on human ESC (hESC) und human induced pluripotent stem cell (hiPSC) derived cardiomyocytes by analyzing the beat rate variability (BRV), which can be considered as the in vitro equivalent of the heart rate variability (HRV) in vivo. Short-term recordings of extracellular field potentials of spontaneously beating cardiomyocytes derived from hESCs and hiPSCs were made using Microelectrode Arrays (MEA). The effect of Flecainide, Ivabradine and Metoprolol was tested. The offline analysis of the BRV was mainly focused on time domain methods. Additionally a non-linear analysis method was used. The evaluation of the Poincaré-Plots of the measurements without pharmacological intervention revealed that the vast majority of the scatter plots have a similar, ellipsoid shape. Flecainide and Ivabradine influenced BRV parameters significantly, whereas Metoprolol did not alter the BRV markedly. We detected remarkable similarities between the BRV of hESC and hiPSC derived cardiomyocytes in vitro and the HRV in vivo. The effect of antiarrhythmic drugs on spontaneously beating cardiomyocytes derived from hESC and hiPSC was generally consistent with clinical experiences and also with our previous study based on murine ESC derived cardiomyocytes. In conclusion, our study points out the great potential of hESC and hiPSC derived tissue to be used routinely for many different applications in medicine and science.

Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal

Previous studies have attempted to find autonomic differences of the cardiac system between the congestive heart failure (CHF) disease and healthy groups using a variety of algorithms of pattern recognition. By comparing previous literature, we have found that there are two shortcomings: 1) Previous studies have focused on improving the accuracy of models, but the number of features used has mostly exceeded 10, leading to poor generalization performance; 2) Previous works rarely distinguish the severity levels of CHF disease. In order to make up for these two shortcomings, we proposed two models: model A was used for distinguishing CHF patients from the normal people; model B was used for diagnosing the four severity levels of CHF disease. Based on long-term heart rate variability (HRV) (40000 intervals–8h) signals, we extracted linear and non-linear features from the inter-beat-interval (IBI) series. After that, the sequence forward selection algorithm (SFS) reduced the feature dimension. Finally, models with the best performance were selected through the leave-one-subject-out validation. For a total of 113 samples of the dataset, we applied the support vector machine classifier and five HRV features for CHF discrimination and obtained an accuracy of 97.35%. For a total of 41 samples of the dataset, we applied k-nearest-neighbor (K = 1) classifier and four HRV features for diagnosing four severity levels of CHF disease and got an accuracy of 87.80%. The contribution in this work was to use the fewer features to optimize our models by the leave-one-subject-out validation. The relatively good generalization performance of our models indicated their value in clinical application.

Prediction of Sudden Cardiac Death in Implantable Cardioverter Defibrillators: A Review and Comparative Study of Heart Rate Variability Features

Over the last four decades, implantable cardioverter defibrillators (ICDs) have been widely deployed to reduce sudden cardiac death (SCD) risk in patients with a history of life-threatening arrhythmia. By continuous monitoring of the heart rate, ICDs can use decision algorithms to distinguish normal cardiac sinus rhythm or supra-ventricular tachycardia from abnormal cardiac rhythms like ventricular tachycardia and ventricular fibrillation and deliver appropriate therapy such as an electrical stimulus. Despite the success of ICDs, more research is still needed, particularly in decision-making algorithms. Because of low specificity in practical devices, patients with ICDs still receive inappropriate shocks, which may lead to inadvertent mortality and reduction of quality of life. At the same time, higher sensitivity can lead to the use of newer tiered therapies. The purpose of this study is to review the literature on common signal features used in detection algorithms for abnormal cardiac sinus rhythm, as well as reviewing datasets used for algorithm development in previous studies. More than 50 different features to address heart rate changes before SCD have been reviewed and general methodology on this area proposed based on variety of studies on ICDs functionality. A comparative study on the prediction performance of these features, using a common database, is also presented. By combining these features with a support vector machine classifier, achieved results have compared well with other studies.

Pitfalls of assessment of autonomic function by heart rate variability

Although analysis of heart rate variability is widely used for the assessment of autonomic function, its fundamental framework linking low-frequency and high-frequency components of heart rate variability with sympathetic and parasympathetic autonomic divisions has developed in the 1980s. This simplified framework is no longer able to deal with much evidence about heart rate variability accumulated over the past half-century. This review addresses the pitfalls caused by the old framework and discusses the points that need attention in autonomic assessment by heart rate variability.

Cardiovascular impact of intravenous caffeine in preterm infants

AIM

To evaluate the acute effect of intravenous caffeine on heart rate and blood pressure variability in preterm infants.

METHODS

We extracted and compared linear and nonlinear features of heart rate and blood pressure variability at two time points: prior to and in the two hours following a loading dose of 10 mg/kg caffeine base.

RESULTS

We studied 31 preterm infants with arterial blood pressure data and 25 with electrocardiogram data, and compared extracted features prior to and following caffeine administration. We observed a reduction in both scaling exponents (α₁ , α₂ ) of mean arterial pressure from detrended fluctuation analysis and an increase in the ratio of short- (SD1) and long-term (SD2) variability from Poincare analysis (SD1/SD2). Heart rate variability analyses showed a reduction in α₁ (mean (SD) of 0.92 (0.21) to 0.86 (0.21), p < 0.01), consistent with increased vagal tone. Following caffeine, beat-to-beat pulse pressure variability (SD) also increased (2.1 (0.64) to 2.5 (0.65) mmHg, p < 0.01).

CONCLUSION

This study highlights potential elevation in autonomic nervous system responsiveness following caffeine administration reflected in both heart rate and blood pressure systems. The observed increase in pulse pressure variability may have implications for caffeine administration to infants with potentially impaired cerebral autoregulation.

Effect of Yoga Intervention on Short-Term Heart Rate Variability in Children with Autism Spectrum Disorder

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in social interactions, communication, restricted, and repetitive behaviors. Evidence-based treatment options for ASD are limited. Yoga is practiced by over 20 million people worldwide, and multiple studies have investigated yoga as a possible effective intervention for children with ASD.

Aim

The aim of this study is to investigate the effect of yoga intervention on short-term heart rate variability (HRV) in children with ASD.

Methodology

In this study, 50 children (38 boys and 12 girls) with ASD were recruited from Swabhimaan Trust, Palavakkam, Chennai. They were randomly grouped into ASD with yoga intervention group (n = 25) and ASD without yoga intervention group (n = 25) by simple lottery method. Yoga group children underwent yoga training for 3 months, and the control group did not receive any such training. For short-term HRV, 15 min electrocardiogram recording in sitting posture was recorded in lead II using a simple analog amplifier.

Results

In HRV, time domain parameters such as mean RR interval (0.72 [0.74] to 0.94 [0.92]), standard deviation of the NN intervals (52.04 [54.23] to 74.48 [72.80]), and root of the mean squared differences of successive NN interval (32.60 [34.40] to 40.83 [42.90]) significantly increased in ASD children after yoga intervention. In frequency-domain parameters, high frequency (HF) in n. u (48.08 [47.24] to 58.37 [59.22]) shows a significant increase and low frequency (LF) in n. u (52.4 [51.82] to 40.51 [40.12]), and LF/HF ratio (1.29 [1.31] to 0.78 [0.79]) shows a significant decrease in ASD with yoga intervention group children after 3 months of yoga training.

Conclusion

Yoga interventions have been successful in bringing parasympathetic dominance in ASD children, and the greater advantage is being a noninvasive way of intervention to support children with ASD and help them to achieve physiological as well as psychological balance.

Poincaré plot analysis of cerebral blood flow signals: Feature extraction and classification methods for apnea detection

OBJECTIVE

Rheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descriptors from rheoencephalography signals to detect apneas in volunteers.

METHODS

A group of 16 subjects participated in the study. Rheoencephalography data from baseline and apnea periods were recorded and Poincaré Plot descriptors were extracted from the reconstructed attractors with different time lags (τ). Among the set of extracted features, those presenting significant differences between baseline and apnea recordings were used as inputs to four different classifiers to optimize the apnea detection.

RESULTS

Three features showed significant differences between apnea and baseline signals: the Poincaré Plot ratio (SDratio), its correlation (R) and the Complex Correlation Measure (CCM). Those differences were optimized for time lags smaller than those recommended in previous works for other biomedical signals, all of them being lower than the threshold established by the position of the inflection point in the CCM curves. The classifier showing the best performance was the classification tree, with 81% accuracy and an area under the curve of the receiver operating characteristic of 0.927. This performance was obtained using a single input parameter, either SDratio or R.

CONCLUSIONS

Poincaré Plot features extracted from the attractors of rheoencephalographic signals were able to track cerebral blood flow changes provoked by breath holding. Even though further validation with independent datasets is needed, those results suggest that nonlinear analysis of rheoencephalography might be a useful approach to assess the correlation of cerebral impedance with clinical changes.

Autonomic Nervous System Function After a Skin-to-Skin Contact Intervention in Infants With Congenital Heart Disease

BACKGROUND

Infants with complex congenital heart disease (CCHD) demonstrate impaired autonomic nervous system (ANS) regulation, which may contribute to commonly observed impairments in feeding, growth, and development. Close mother-infant physical contact improves ANS function in other high-risk infant populations.

OBJECTIVE

Our purposes were to examine feasibility of a 2-week daily skin-to-skin contact (SSC) intervention and to describe change in ANS function in response to the intervention.

METHODS

Feasibility was assessed by recruitment and retention rates, safety, and acceptability. Autonomic nervous system function was measured with linear and nonlinear measures of heart rate variability (HRV) during a feeding before the 14-day SSC intervention and 3 times during a 4-week follow-up.

RESULTS

Recruitment rate was 72%; retention rate was 55.5%. Mothers were universally positive about SSC. Ten mothers and infants completed a daily mean of 77.05 minutes of SSC during the 14-day intervention with no adverse events. Baseline high-frequency HRV, reactivity to challenge, and recovery after challenge improved during the 6-week study. Nonlinear measures demonstrated abnormally high sympathetic activity, especially after feeding, in most infants.

CONCLUSIONS

Skin-to-skin contact is feasible in infants with CCHD. Linear measures of HRV suggested improvements in ANS function with this intervention. This article is one of the first to describe nonlinear HRV measures in infants with CCHD. Additional research is needed to identify variations in linear and nonlinear effects based on specific cardiac conditions and with varying intervention doses and timing. This low-cost, low-risk intervention has the potential to improve outcomes in infants with CCHD.

Nonlinear analyses of heart rate variability in hypertension

OBJECTIVE

The autonomic nervous system plays an important role in blood pressure regulation and in the development of hypertension. Heart rate variability (HRV) may be of importance in identifying subjects at higher risk of developing hypertension. In the present study, comparative analysis of HRV for normal and hypertension subjects using nonlinear indices has been carried out.

PATIENTS AND METHODS

We analyzed the data from 24 patients with essential hypertension aged 45-55 (HG). All the subjects in this group had a confirmed diagnosis of the first- or second-stage essential hypertension. The control group for these subjects included 32 healthy volunteers of the same age group (CG). We analyzed the whole 5minutes of the ECG recording. Nonlinear indices D₂, K₂ and lagged Poincaré plot indices were calculated.

RESULTS

Values of D₂ and K₂ in hypertension state is statistically significantly lower than in healthy. Poincaré plot indices SD1 and SD2 in healthy volunteers are statistically higher than in hypertensive patients for all lags (P<0,05). SD1/SD2 ratio is also higher in healthy subjects, but the difference is statistically significant only for l=5 and 6.

CONCLUSION

The heart rhythm in groups with hypertension is more regular, its dynamics are less complex and less "chaotic," compared to healthy ones. This is expressed in lower values of entropy and correlation dimension. The HR stabilization is observed at both short and long time intervals, but mainly due to the decrease in the difference of the "neighboring" R-R intervals.

Does the Temporal Asymmetry of Short-Term Heart Rate Variability Change during Regular Walking? A Pilot Study of Healthy Young Subjects

The acceleration and deceleration patterns in heartbeat fluctuations distribute asymmetrically, which is known as heart rate asymmetry (HRA). It is hypothesized that HRA reflects the balancing regulation of the sympathetic and parasympathetic nervous systems. This study was designed to examine whether altered autonomic balance during exercise can lead to HRA changes. Sixteen healthy college students were enrolled, and each student undertook two 5-min ECG measurements: one in a resting seated position and another while walking on a treadmill at a regular speed of 5 km/h. The two measurements were conducted in a randomized order, and a 30-min rest was required between them. RR interval time series were extracted from the 5-min ECG data, and HRA (short-term) was estimated using four established metrics, that is, Porta's index (PI), Guzik's index (GI), slope index (SI), and area index (AI), from both raw RR interval time series and the time series after wavelet detrending that removes the low-frequency component of <~0.03 Hz. Our pilot data showed a reduced PI but unchanged GI, SI, and AI during walking compared to resting seated position based on the raw data. Based on the wavelet-detrended data, reduced PI, SI, and AI were observed while GI still showed no significant changes. The reduced PI during walking based on both raw and detrended data which suggests less short-term HRA may underline the belief that vagal tone is withdrawn during low-intensity exercise. GI may not be sensitive to short-term HRA. The reduced SI and AI based on detrended data suggest that they may capture both short- and long-term HRA features and that the expected change in short-term HRA is amplified after removing the trend that is supposed to link to long-term component. Further studies with more subjects and longer measurements are warranted to validate our observations and to examine these additional hypotheses.