Comparison of nonlinear methods symbolic dynamics, detrended fluctuation, and Poincare plot analysis in risk stratification in patients with dilated cardiomyopathy

Development of autonomic heart rate modulations during childhood and adolescence

Autonomic control of heart rate is well known in adult subjects, but limited data are available on the development of the heart rate control during childhood and adolescence. Continuous 12-lead electrocardiograms were recorded in 1045 healthy children and adolescents (550 females) aged 4 to 19 years during postural manoeuvres involving repeated 10-min supine, unsupported sitting, and unsupported standing positions. In each position, heart rate was measured, and heart rate variability indices were evaluated (SDNN, RMSSD, and high (HF) and low (LF) frequency components were obtained). Quasi-normalized HF frequency components were defined as qnHF = HF/(HF + LF). These measurements were, among others, related to age using linear regressions. In supine position, heart rate decreases per year of age were significant in both sexes but lower in females than in males. In standing position, these decreases per year of age were substantially lowered. RMSSD and qnHF indices were independent of age in supine position but significantly decreased with age in sitting and standing positions. Correspondingly, LF/HF proportions showed steep increases with age in sitting and standing positions but not in the supine position. The study suggests that baseline supine parasympathetic influence shows little developmental changes during childhood and adolescence but that in young children, sympathetic branch is less responsive to vagal influence. While vagal influences modulate cardiac periods in young and older children equally, they are less able to suppress the sympathetic influence in younger children.

Intra-Individual Relationship between Heart Rate Variability and the Underlying Heart Rate in Children and Adolescents

Background/Objective: The relationship between heart rate and heart rate variability (HRV) indices has been repeatedly studied in adults but limited data are available on the relationship in paediatric populations. Methods: Continuous 12-lead electrocardiograms were recorded in 1016 healthy children and adolescents (534 females) aged 4 to 19 years during postural manoeuvres with rapid changes between 10-min positions of supine → sitting → standing → supine → standing → sitting → supine. In each position, the averaged RR interval was measured together with four HRV indices, namely the SDNN, RMSSD, quasi-normalised high-frequency components (qnHF), and the proportions of low- and high-frequency components (LF/HF). In each subject, the slope of the linear regression between the repeated HRV measurements and the corresponding RR interval averages was calculated. Results: The intra-subject regression slopes, including their confidence intervals, were related to the age and sex of the subjects. The SDNN/RR, RMSSD/RR, and qnHF/RR slopes were significantly steeper (p < 0.001) and the (LF/HF)/RR slopes were significantly shallower (p < 0.001) in younger children compared to older children and adolescents. Conclusions: The study suggests that sympathetic and vagal influences on heart rate are present in both younger and older children. With advancing age, the sympatho-vagal balance gradually develops and allows the vagal control to suppress the sympathetic drive towards higher heart rates seen in younger age children.

Assessment of the human response to acute mental stress-An overview and a multimodal study

Numerous vital signs are reported in association with stress response assessment, but their application varies widely. This work provides an overview over methods for stress induction and strain assessment, and presents a multimodal experimental study to identify the most important vital signs for effective assessment of the response to acute mental stress. We induced acute mental stress in 65 healthy participants with the Mannheim Multicomponent Stress Test and acquired self-assessment measures (Likert scale, Self-Assessment Manikin), salivary α-amylase and cortisol concentrations as well as 60 vital signs from biosignals, such as heart rate variability parameters, QT variability parameters, skin conductance level, and breath rate. By means of statistical testing and a self-optimizing logistic regression, we identified the most important biosignal vital signs. Fifteen biosignal vital signs related to ventricular repolarization variability, blood pressure, skin conductance, and respiration showed significant results. The logistic regression converged with QT variability index, left ventricular work index, earlobe pulse arrival time, skin conductance level, rise time and number of skin conductance responses, breath rate, and breath rate variability (F1 = 0.82). Self-assessment measures indicated successful stress induction. α-amylase and cortisol showed effect sizes of -0.78 and 0.55, respectively. In summary, the hypothalamic-pituitary-adrenocortical axis and sympathetic nervous system were successfully activated. Our findings facilitate a coherent and integrative understanding of the assessment of the stress response and help to align applications and future research concerning acute mental stress.

Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder

Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

Cuffless Blood Pressure Monitoring from an Array of Wrist Bio-Impedance Sensors Using Subject-Specific Regression Models: Proof of Concept

Continuous and beat-to-beat monitoring of blood pressure (BP), compared to office-based BP measurement, provides significant advantages in predicting future cardiovascular disease. Traditional BP measurement methods are based on a cuff, which is bulky, obtrusive and not applicable to continuous monitoring. Measurement of pulse transit time (PTT) is one of the prominent cuffless methods for continuous BP monitoring. PTT is the time taken by the pressure pulse to travel between two points in an arterial vessel, which is correlated with the BP. In this paper, we present a new cuffless BP method using an array of wrist-worn bio-impedance sensors placed on the radial and the ulnar arteries of the wrist to monitor the arterial pressure pulse from the blood volume changes at each sensor site. BP is accurately estimated by using AdaBoost regression model based on selected arterial pressure pulse features such as transit time, amplitude and slope of the pressure pulse, which are dependent on the cardiac activity and the vascular properties of the wrist arteries. A separate model is developed for each subject based on calibration data to capture the individual variations of BP parameters. In this pilot study, data was collected from 10 healthy participants with age ranges from 18 to 30 years after exercising using our custom low-noise bio-impedance sensing hardware. Post-exercise BP was accurately estimated with an average correlation coefficient and root mean square error (RMSE) of 0.77 and 2.6 mmHg for the diastolic BP and 0.86 and 3.4 mmHg for the systolic BP.

The entropy of RR intervals is associated to gestational age in full-term newborns with adequate weight for gestational age

Introduction: The variability of successive RR intervals has been pointed out as an indicator of systemic homeostasis. The entropy of successive RR intervals is associated with a greater adaptive capacity, which is essential after childbirth, characterized by a change from an intrauterine environment that constantly adapts to the fetal demands, to an extrauterine environment that requires constant biological adaptations by the neonate.Objectives: To analyze the association between gestational age (GA) and the entropy of RR intervals in term infants with adequate birth weight in the first hours of extrauterine life.Methods: In a cross-sectional study design maternal, labor and neonatal characteristics were collected from the obstetric records. Successive RR intervals were recorded from neonates up to 72 hours (i.e. 3 days) of birth.Subjects: Fifty term infants, healthy and with adequate birth weight. Outcome measures: the variability of RR intervals was analyzed obtaining the entropy of 1000 successive RR intervals. Pearson's correlation was used to evaluate the association between GA and the entropy of successive RR intervals, while linear regression was used to obtain the coefficient of determination (r²) as well as a prediction model. The adequacy of the prediction model was evaluated using the Komolgorov-Smirnov test to evaluate the residuals distribution.Results: There was a positive and significant association between the studied variables (r = 0.437; p = .002). The coefficient of determination allowed us to infer that approximately 19.3% of the RR interval entropy from the studied sample can be explained by the GA (r² = 0.193; p = .002). The analysis of the residuals distribution confirmed that the regression model obtained here was adequate.Conclusion: Our results indicate that, even within a normal range of GA (≥37 a < 42 weeks) and birth weight, a longer intrauterine life allows a greater entropy of successive RR intervals, indicating a greater maturation of biological systems and adaptive capacity.

Periodized Aerobic Interval Training Modifies Geometric Indices of Heart Rate Variability in Metabolic Syndrome

Background: Metabolic syndrome (MetS) influences the autonomic modulation, increasing the risk of cardiovascular events, which demands the identification of effective treatments for this population. Considering this, the study has the objective of evaluating the effects of periodized aerobic interval training (AIT) on geometrical methods of heart rate variability (HRV) on individuals with MetS. Methods: 52 individuals with MetS were considered for analysis. They were divided into two groups: aerobic interval training group (AITG; n = 26) and control group (CG; n = 26). The AITG performed 16 weeks of periodized AIT. For HRV analysis, the heart rate was recorded beat-by-beat at the beginning and the end of the AIT program and geometrical methods were used for analysis. Results: significant increase was observed for triangular index (RRtri, -1.25 ± 0.58 vs. 1.41 ± 0.57), standard deviation of distances from diagonal to points (SD1, -0.13 ± 1.52 vs. 4.34 ± 1.49), and standard deviation of distances from points to lines (SD2, -2.14 ± 3.59 vs. 11.23 ± 3.52) on AITG compared to CG. Significant differences were not observed for triangular interpolation of normal heartbeats interval histogram (TINN, -4.05 ± 17.38 vs. 25.52 ± 17.03) and SD1/SD2 ratio (0.03 ± 0.02 vs. 0.00 ± 0.02). Qualitative analysis of the Poincaré plot identified increase on dispersion of both short and long-term intervals between successive heartbeats (RR interval) on AITG after the AIT program. Conclusion: geometric indices of HRV suggest an increase in cardiac autonomic modulation in individuals with MetS after 16 weeks of periodized AIT.

Heart rate variability: are you using it properly? Standardisation checklist of procedures

BACKGROUND

Heart rate variability is used as an assessment method for cardiac autonomic modulation. Since the Task Force's publication on heart rate variability in 1996, the European Heart Rhythm Association Position Paper in 2015 and a recent publication in 2017, attention has been paid to recommendations on using heart rate variability analysis methods, as well as their applications in different physiological conditions and clinical studies. This analysis has proved to be useful as a complementary tool for clinical evaluation and to assess the effect of non-pharmacological therapeutic interventions, such as physical exercise programmes, on cardiac autonomic modulation.

OBJECTIVE

The aim of this article is to make recommendations and to develop a checklist of normalisation procedures regarding the use of heart rate variability data collection and analysis methodology, focusing on the cardiology area and cardiac rehabilitation.

METHODS

Based on previous heart rate variability publications, this paper provides a description of the most common shortcomings of using the analysis methods and considers recommendations and suggestions on how to minimise these occurrences by using a specific checklist.

CONCLUSIONS

This article includes recommendations and a checklist regarding the use of heart rate variability collection and analysis methods. This work could help improve reporting on clinical evaluation and therapeutic intervention results and consequently, disseminate heart rate variability knowledge.

Respiratory Sinus Arrhythmia Quantified with Linear and Non-Linear Techniques to Classify Dilated and Ischemic Cardiomyopathy

In congestive heart failure (CHF), dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two highly related pathologies that are not fully characterized. The aim of this study is to assess respiratory sinus arrhythmia (RSA) index of the parasympathetic system, in order to discriminate between both pathologies, DCM and ICM. For this, ECG-signals of 49 subjects (12 DCM patients, 21 ICM patients, 6 ICM patients with diabetes mellitus (DM) type II and 10 control subjects) from the database HERIS II and of 173 subjects (50 DCM, 50 ICM, 15 DCM with DM type II, 15 ICM with DM type II and 47 control subjects) from the database MUSIC2 were analyzed. The RSA was quantified using linear and non-linear analysis methods (fractal dimension and entropy). The results showed a significant difference between ICM and DCM subjects (p=0.013) with a sensitivity of 83% and specificity of 90%. Decreasing RSA values were present in CHF patients, especially in ICM patients, in comparison with healthy subjects. Alterations in the parasympathetic system due to DM were also identified.

Rhythmical massage improves autonomic nervous system function: a single-blind randomised controlled trial

BACKGROUND

Rhythmical massage therapy (RMT) is a massage technique used in anthroposophic medicine.

OBJECTIVE

The authors aimed to investigate the physiological action of RMT on the cardiovascular system by analysing heart rate variability (HRV).

DESIGN, SETTING, PARTICIPANTS AND INTERVENTION

This study was a randomised, controlled and single-blinded trial, involving 44 healthy women (mean age: (26.20 ± 4.71) years). The subjects were randomised to one of three arms: RMT with aromatic oil (RA), RMT without aromatic oil (RM) or standardised sham massage (SM). In the study the subjects were exposed to a standardised stress situation followed by one of the study techniques and Holter electrocardiograms (ECGs) were recorded for 24 h.

MAIN OUTCOME MEASURES

HRV parameters were calculated from linear (time and frequency domain) and nonlinear dynamics (symbolic dynamics, Poincare plot analysis) of the 24-h Holter ECG records.

RESULTS

Short- and long-term effects of massage on autonomic regulation differed significantly among the three groups. Immediately after an RMT session, stimulation of HRV was found in the groups RA and RM. The use of an aromatic oil produced greater short-term measurable changes in HRV compared with rhythmic massage alone, but after 24 h the effect was no longer distinguishable from the RM group. The lowest stimulation of HRV parameters was measured in the SM group.

CONCLUSION

RMT causes specific and marked stimulation of the autonomic nervous system. Use of a medicinal aromatic oil had only a temporary effect on HRV, indicating that the RM causes the most relevant long-term effect. The effect is relatively specific, as the physiological effects seen in the group of subjects who received only SM were considerably less pronounced.

TRIAL REGISTRATION

Registration trial DRKS00004164 on DRKS.

Sarcopenia is associated to an impaired autonomic heart rate modulation in community-dwelling old adults

PURPOSE

The aims of this study were to compare the autonomic heart control parameters from sarcopenic and non-sarcopenic community-dwelling elders.

METHODS

This is a cross-sectional study including 76 community-dwelling old adults, which was clinically stratified as sarcopenic or non-sarcopenic, according to the current recommendations. They were submitted to 5-min recordings of successive RR intervals. The analysis of the RR intervals variability was carried out in time (mean RR, RMSSD, pNN50, SDNN and triangular index) and frequency domains (LFnu, HFnu and LF/HF ratio), and with nonlinear methods (SD1, SD2, and D₂). The parameters of autonomic heart rate modulation (AHRM) were adjusted for potential confounders: sex, diabetes, beta-blockers use, cardiovascular disease, body mass index and physical activity level, smoking habit. Normality of the data was tested by Kolmogorov-Smirnov test and, since most variables did not exhibit a normal distribution the Mann-Whitney test was used to compare the parameters of AHRM. The significance level was set as p ≤ 0.05 and all statistical procedures were performed with SPSS^®.

RESULTS

Adjusted parameters of AHRM obtained from time domain and nonlinear methods were significantly different between sarcopenic and non-sarcopenic elders (p < 0.05), while parameters obtained from frequency domain analysis did not were different between groups (p > 0.05).

CONCLUSION

Sarcopenic old adults exhibited lower parasympathetic-associated modulation, suggesting a poor cardioprotection associated to this condition.

Analysis of blood pressure signal in patients with different ventricular ejection fraction using linear and non-linear methods

Changes in the left ventricle function produce alternans in the hemodynamic and electric behavior of the cardiovascular system. A total of 49 cardiomyopathy patients have been studied based on the blood pressure signal (BP), and were classified according to the left ventricular ejection fraction (LVEF) in low risk (LR: LVEF>35%, 17 patients) and high risk (HR: LVEF≤35, 32 patients) groups. We propose to characterize these patients using a linear and a nonlinear methods, based on the spectral estimation and the recurrence plot, respectively. From BP signal, we extracted each systolic time interval (STI), upward systolic slope (BPsl), and the difference between systolic and diastolic BP, defined as pulse pressure (PP). After, the best subset of parameters were obtained through the sequential feature selection (SFS) method. According to the results, the best classification was obtained using a combination of linear and nonlinear features from STI and PP parameters. For STI, the best combination was obtained considering the frequency peak and the diagonal structures of RP, with an area under the curve (AUC) of 79%. The same results were obtained when comparing PP values. Consequently, the use of combined linear and nonlinear parameters could improve the risk stratification of cardiomyopathy patients.

Identifying glaucoma patients by applying multivariate analyses of cardiovascular signals

Glaucoma is a disease that damages the eye's optic nerve. However, the exact cause of this optic nerve damage is not yet fully understood. Besides the factors of age, genetics and others, such as obesity, medication and migraines, a vascular dysfunction is believed to be a significant factor leading to glaucoma. This study's objective was to investigate whether these vascular dysfunctions could be recognized by analyzing cardiovascular regulation in glaucoma patients. Linear and nonlinear methods were applied to the extracted heart rate (HR), and systolic/ diastolic blood pressure (DBP) time series to discriminate between 35 healthy controls and 20 glaucoma patients. The combination of indices from 30-min analysis of time domain (Renyi entropy of systolic blood pressure) and nonlinear dynamics (segmented Poincare plot analysis of DBP, high-resolution joint symbolic dynamics of DBP/ HR) were able to differentiate between controls and patients with a specificity and sensitivity of > 95%. Since changes in short-term blood pressure regulation patterns and heart-rate coupling are clear signs of a vascular dysfunction, this approach could be useful for providing an earlier diagnosis of glaucoma in clinical practice.

Application of Heart Rate Variability in Diagnosis and Prognosis of Individuals with Diabetes Mellitus: Systematic Review

BACKGROUND

The use of heart rate variability as a tool capable of discriminating individuals with diabetes mellitus is still little explored, as its use has been limited to comparing those with and without the disease. Thus, the purpose of this study was to verify the use of heart rate variability as a tool for diagnostic and prognostic evaluation in person with diabetes and to identify whether there are cutoff points generated from the use of this tool in these individuals.

METHODS

A search was conducted in the electronic databases MEDLINE, Cochrane Library, Web of Science, EMBASE, and LILACS starting from the oldest records until January 2015, by means of descriptors related to the target condition, evaluated tool, and evaluation method. All the studies were evaluated for methodological quality using the QUADAS-2 instrument.

RESULTS

Eight studies were selected. In general, the studies showed that the heart rate variability is useful to discriminate cardiac autonomic neuropathy in person with diabetes, and the sample entropy, SD1/SD2 indices, SDANN, HF, and slope of TFC have better discriminatory power to detect autonomic dysfunction, with sensitivity and specificity values ranging from 72% to 100% and 71% to 97%, respectively.

CONCLUSION

Although there are methodological differences in indices used, in general, this tool demonstrated good sensitivity and specificity and can be used as an additional and/or complementary tool to the conventional autonomic tests, in order to obtain safer and more effective diagnostic, collaborating for better risk stratification conditions of these patients.

Symbolic features and classification via support vector machine for predicting death in patients with Chagas disease

This paper introduces a technique for predicting death in patients with Chagas disease using features extracted from symbolic series and time-frequency indices of heart rate variability (HRV). The study included 150 patients: 15 patients who died and 135 who did not. The HRV series were obtained from 24-h Holter monitoring. Sequences of symbols from 5-min epochs from series of RR intervals were generated using symbolic dynamics and ordinal pattern statistics. Fourteen features were extracted from symbolic series and four derived from clinical aspects of patients. For classification, the 18 features from each epoch were used as inputs in a support vector machine (SVM) with a radial basis function (RBF) kernel. The results showed that it is possible to distinguish between the two classes, patients with Chagas disease who did or did not die, with a 95% accuracy rate. Therefore, we suggest that the use of new features based on symbolic series, coupled with classic time-frequency and clinical indices, proves to be a good predictor of death in patients with Chagas disease.

Reliability analysis of the heart autonomic control parameters during hemodialysis sessions

The study of heart autonomic control (HAC) in patients with chronic kidney disease (CKD) undergoing dialysis treatment has been carried out, however, there are no studies reporting the reliability of measurements of HAC parameters involving the mentioned samples and conditions. The reliability of many HAC parameters was evaluated from patients with CKD during two sessions of hemodialysis. The successive R-R intervals were recorded during two sessions of hemodialysis from 14 CKD patients that were undergoing dialysis for at least 6 months and with no history of recurrent hypotensive events. HAC parameters were obtained with time and frequency domain analysis, as well as with nonlinear methods. The reliability was measured with the intraclass correlation coefficient (ICC). The results showed excellent reliability (ICC=0.90–0.98) for most heart rate variability (HRV) parameters, especially the parameters obtained in the time domain [square root of the mean squared differences between successive R-R intervals (RMSSD), percentage of adjacent R-R intervals that differ by more than 50 ms (pNN50), mean of the 5-min standard deviations of R-R intervals (SDNNi), and triangular index] and with non-linear methods [standard deviation of the instantaneous variability beat-to-beat (SD1), standard deviation in long-term continuous R-R intervals (SD2), detrended fluctuation analysis (DFA) α1 and α2, approximate and sample entropies, and correlation dimension (D2): ICC=0.86–0.96]. Among the parameters obtained in the frequency domain (normalized magnitude from the spectrum of low-frequency components (LFnu), normalized magnitude from the spectrum of high-frequency components (HFnu), and LF/HF ratio), the LF/HF ratio showed better reliability (ICC=0.96 vs. ICC=0.70). Measurements of HAC parameters have excellent test-retest reliability for the studied samples and conditions.

Blood Pressure and Heart Rate Variability to Detect Vascular Dysregulation in Glaucoma

Purpose. To investigate blood pressure and heart rate variability in patients with primary open-angle glaucoma (POAG) to detect disturbed blood pressure regulation. Methods. Thirty-one patients with POAG (mean age 68 ± 10 years) and 48 control subjects (mean age 66 ± 10 years) were included in a prospective study. Continuous blood pressure and heart rate were simultaneously and noninvasively recorded over 30 min (Glaucoscreen, aviant GmbH, Jena, Germany). Data were analyzed calculating univariate linear (time domain and frequency domain), nonlinear (Symbolic Dynamics, SD) and bivariate (Joint Symbolic Dynamics, JSD) indices. Results. Using nonlinear methods, glaucoma patients were separated with more parameters compared to linear methods. In POAG, nonlinear univariate indices (pW113 and pW120_Sys) were increased while the indices pTH10_Sys and pTH11_Sys reflect a reduction of dominant patterns. Bivariate indices (JSDdia29, JSDdia50, and JSDdia52; coupling between heart rate and diastolic blood pressure) were increased in POAG. The optimum set consisting of six parameters (JSDdia29, JSDdia58, pTH9_Sys, pW231, pW110_Sys and pW120_Sys) revealed a sensitivity of 83.3% and specificity of 80.6%. Conclusions. Nonlinear uni- and bivariate indices of continuous recordings of blood pressure and heart rate are altered in glaucoma. Abnormal blood pressure variability suggests disturbed autonomic regulation in patients with glaucoma.

QT variability improves risk stratification in patients with dilated cardiomyopathy

Recently it could be demonstrated that systolic and diastolic blood pressure variability (BPV) as well as segmented Poincare plot analysis (SPPA) contribute to risk stratification in patients suffering from dilated cardiomyopathy (DCM). The aim of this study was to improve the risk stratification applying a multivariate technique including QT variability (QTV). We enrolled and significantly separated 56 low risk and 13 high risk DCM patients by nearly all applied BPV and QTV methods, but not with traditional heart rate variability analysis. The optimum set of two indices calculating the multivariate discriminate analysis (DA) included one BPV index calculated by symbolic dynamics method (DBP(Shannon)) and one index calculated from QTV (QTV(log)) achieving an area under the receiver operating characteristics curve (AUC) of 92%, sensitivity of 92.3% and specificity of 89.3%. Performing only electrocardiogram analysis, the optimum multivariate approach including indices from segmented Poincaré plot analysis and QTV still achieved a remarkable AUC of 88.3%. Increasing the number of indices for multivariate DA up to three, we achieved an AUC of 95.7%, sensitivity of 100% and specificity of 85.7% including one clinical, one BPV and one QTV index. Summarizing, we identified DCM patients with an increased risk of sudden cardiac death applying QTV analysis in a multivariate approach.

[Nonischemic dilated cardiomyopathy. Parameters of autonomic tone]

BACKGROUND

Nonischemic dilated cardiomyopathies (DCM) are the most common reason for heart failure in developed countries after ischemic disease. They often lead to device therapy. Left ventricular ejection fraction as a single parameter to identify patients at risk for sudden cardiac death revealed inconclusive data in patients with DCM.

METHODS

Autonomic tone, measured by classical and innovative parameters of heart rate variability (HRV), heart rate turbulence or baroreceptor reflex, was demonstrated to give valuable prognostic information especially in patients with ischemic disease and after acute myocardial infarction. In patients with DCM, classical parameters of HRV showed inhomogeneous data in a heterogeneous patient collective caused by unsystematic measurement of single parameters in various patient collectives.

RESULTS

Innovative parameters of HRV are promising in patients with DCM and showed prognostic relevance although patient numbers are limited and prospective data are missing. Further studies are needed in this field.

CONCLUSION

Despite the in part convincing evidence for the relevance of autonomic tone as a prognostic marker in patients with DCM, their evaluation is still not part of clinical routine. Additional parameters to estimate the risk of sudden cardiac death are urgently needed.

Tracking instantaneous entropy in heartbeat dynamics through inhomogeneous point-process nonlinear models

Measures of entropy have been proved as powerful quantifiers of complex nonlinear systems, particularly when applied to stochastic series of heartbeat dynamics. Despite the remarkable achievements obtained through standard definitions of approximate and sample entropy, a time-varying definition of entropy characterizing the physiological dynamics at each moment in time is still missing. To this extent, we propose two novel measures of entropy based on the inho-mogeneous point-process theory. The RR interval series is modeled through probability density functions (pdfs) which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed measures of entropy are instantaneously defined through such probability functions, the proposed indices are able to provide instantaneous tracking of autonomic nervous system complexity. Of note, the distance between the time-varying phase-space vectors is calculated through the Kolmogorov-Smirnov distance of two pdfs. Experimental results, obtained from the analysis of RR interval series extracted from ten healthy subjects during stand-up tasks, suggest that the proposed entropy indices provide instantaneous tracking of the heartbeat complexity, also allowing for the definition of complexity variability indices.

How to Calculate Renyi Entropy from Heart Rate Variability, and Why it Matters for Detecting Cardiac Autonomic Neuropathy

Cardiac autonomic neuropathy (CAN) is a disease that involves nerve damage leading to an abnormal control of heart rate. An open question is to what extent this condition is detectable from heart rate variability (HRV), which provides information only on successive intervals between heart beats, yet is non-invasive and easy to obtain from a three-lead ECG recording. A variety of measures may be extracted from HRV, including time domain, frequency domain, and more complex non-linear measures. Among the latter, Renyi entropy has been proposed as a suitable measure that can be used to discriminate CAN from controls. However, all entropy methods require estimation of probabilities, and there are a number of ways in which this estimation can be made. In this work, we calculate Renyi entropy using several variations of the histogram method and a density method based on sequences of RR intervals. In all, we calculate Renyi entropy using nine methods and compare their effectiveness in separating the different classes of participants. We found that the histogram method using single RR intervals yields an entropy measure that is either incapable of discriminating CAN from controls, or that it provides little information that could not be gained from the SD of the RR intervals. In contrast, probabilities calculated using a density method based on sequences of RR intervals yield an entropy measure that provides good separation between groups of participants and provides information not available from the SD. The main contribution of this work is that different approaches to calculating probability may affect the success of detecting disease. Our results bring new clarity to the methods used to calculate the Renyi entropy in general, and in particular, to the successful detection of CAN.

Mathematical biomarkers for the autonomic regulation of cardiovascular system

Heart rate and blood pressure are the most important vital signs in diagnosing disease. Both heart rate and blood pressure are characterized by a high degree of short term variability from moment to moment, medium term over the normal day and night as well as in the very long term over months to years. The study of new mathematical algorithms to evaluate the variability of these cardiovascular parameters has a high potential in the development of new methods for early detection of cardiovascular disease, to establish differential diagnosis with possible therapeutic consequences. The autonomic nervous system is a major player in the general adaptive reaction to stress and disease. The quantitative prediction of the autonomic interactions in multiple control loops pathways of cardiovascular system is directly applicable to clinical situations. Exploration of new multimodal analytical techniques for the variability of cardiovascular system may detect new approaches for deterministic parameter identification. A multimodal analysis of cardiovascular signals can be studied by evaluating their amplitudes, phases, time domain patterns, and sensitivity to imposed stimuli, i.e., drugs blocking the autonomic system. The causal effects, gains, and dynamic relationships may be studied through dynamical fuzzy logic models, such as the discrete-time model and discrete-event model. We expect an increase in accuracy of modeling and a better estimation of the heart rate and blood pressure time series, which could be of benefit for intelligent patient monitoring. We foresee that identifying quantitative mathematical biomarkers for autonomic nervous system will allow individual therapy adjustments to aim at the most favorable sympathetic-parasympathetic balance.

Ischemic risk stratification by means of multivariate analysis of the heart rate variability

In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures.

Effect of missing RR-interval data on nonlinear heart rate variability analysis

The effects of missing RR-interval data on nonlinear heart rate variability (HRV) analysis were investigated using simulated missing data in actual RR-interval tachograms and actual missing RR-interval data. For the simulation study, randomly selected data (ranging from 0 to 100s) were removed from actual data in the MIT-BIH normal sinus rhythm RR-interval database. The selected data are considered as a simulated artefact section. In all, 7182 tachograms of 5-min duration were used for this analysis. For each missing interval, the analysis was performed by 100 Monte Carlo runs. Poincaré plot, detrended fluctuation, and entropy analysis were executed for the nonlinear HRV parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters, were calculated. In this process, the usefulness of reconstruction was considered, for which bootstrapping and several interpolation methods (nearest neighbour, linear, cubic spline, and piecewise cubic Hermite) were used. The rules for the reconstruction, derived from the results of these simulations, were evaluated with actual missing RR-interval data obtained from a capacitive-coupled ECG during sleep. In conclusion, nonlinear parameters, excepting Poincaré-plot-analysis parameters, may not be appropriate for the accurate HRV analysis with missing data, since these parameters have relatively larger error values than time- or frequency-domain HRV parameters. However, the analysis of the long-term variation for nonlinear HRV values can be available through applying the rules for the reconstruction obtained in this study.

Poincaré plot indexes of heart rate variability detect dynamic autonomic modulation during general anesthesia induction

PURPOSE

Beat-to-beat heart rate variability (HRV) is caused by the fluctuating balance of sympathetic and parasympathetic tone. The Poincaré plot has been used to evaluate HRV. In this study, we validate that this new method may qualitatively and quantitatively assess the sympathovagal fluctuation in patients during induction of anesthesia with sevoflurane.

METHODS

Twenty-eight young patients were allocated for the study. The patients received a tilt test and on the next day they sustained anesthesia induced with inhaled anesthetics. Electrocardiography signals from the patients were relayed to an analogue-digital converter. The Poincaré plot is quantified by measuring SD1, SD2, and SD1/SD2. Power spectral analyses were performed and LF, HF and HF/LF were calculated.

RESULTS

The LF power and the SD2 of the Poincaré plot increased while subjects were tilt-up from the supine position. Additionally, a significant correlation were found between LF and SD2, HF and SD1 (p < 0.05), and LF/HF and SD2/SD1 (p < 0.01). Sevoflurane inhalation for 10 minutes had no effect on heart rate, but diminished LF, total power and SD1, SD2 of the Poincaré plot respectively. However, the LF, SD2 and LF/HF increased; the HF, SD1 and SD1/SD2 ratio decreased after intubation stimulation.

CONCLUSION

Poincaré plot and power spectral analysis of HRV during tilt test and sevoflurane induction significantly correlate. Poincaré plot analysis is easier and more sensitive at evaluating the sympathovagal balance and observing the beat-to-beat HRV.

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.

Quantification of compensatory processes of postnatal hypoxia in newborn piglets applying short-term nonlinear dynamics analysis

Background

Newborn mammals suffering from moderate hypoxia during or after birth are able to compensate a transitory lack of oxygen by adapting their vital functions. Exposure to hypoxia leads to an increase in the sympathetic tone causing cardio-respiratory response, peripheral vasoconstriction and vasodilatation in privileged organs like the heart and brain. However, there is only limited information available about the time and intensity changes of the underlying complex processes controlled by the autonomic nervous system.

Methods

In this study an animal model involving seven piglets was used to examine an induced state of circulatory redistribution caused by moderate oxygen deficit. In addition to the main focus on the complex dynamics occurring during sustained normocapnic hypoxia, the development of autonomic regulation after induced reoxygenation had been analysed. For this purpose, we first introduced a new algorithm to prove stationary conditions in short-term time series. Then we investigated a multitude of indices from heart rate and blood pressure variability and from bivariate interactions, also analysing respiration signals, to quantify the complexity of vegetative oscillations influenced by hypoxia.

Results

The results demonstrated that normocapnic hypoxia causes an initial increase in cardiovascular complexity and variability, which decreases during moderate hypoxia lasting one hour (p < 0.004). After reoxygenation, cardiovascular complexity parameters returned to pre-hypoxic values (p < 0.003), however not respiratory-related complexity parameters.

Conclusions

In conclusion, indices from linear and nonlinear dynamics reflect considerable temporal changes of complexity in autonomous cardio-respiratory regulation due to normocapnic hypoxia shortly after birth. These findings might be suitable for non-invasive clinical monitoring of hypoxia-induced changes of autonomic regulation in newborn humans.

[Geometric index of heart rate variability in chronic obstructive pulmonary disease]

BACKGROUND

It was already evidenced decreased heart rate variability (HRV) in chronic obstructive pulmonary disease (COPD) patients at rest.

OBJECTIVE

In order to insert new elements in the literature regarding this issue, we evaluated geometric index of HRV in COPD subjects.

METHOD

We analyzed data from 34 volunteers, divided into two groups according to spirometric values: COPD (17 volunteers, FEV1/FVC=47.3±10.2; FEV1=50.8±15.7) and control (17 volunteers, FEV1/FVC=78.8±10.8; FEV1=100.1±14.7). For analysis of HRV indexes the volunteers remained in the supine position for 30 minutes. We analyzed the following indexes: triangular index (RRtri), triangular interpolation of RR intervals (TINN) and Poincaré plot (SD1, SD2 and SD1/SD2). Student t test for unpaired samples and Mann-Whitney test were used for data analysis.

RESULTS

We observed statistically significant reductions in geometric indexes in the COPD group: RRtri (0.043±0.01 vs. 0.059±0.02; p=0.018), TINN (105.88±51.82 vs. 151.47±49.9; p=0.014), SD1 (9.76±4.66 vs. 14.55±6.04; p=0.014) and SD2 (34.86±17.02 vs. 51.51±18.38; p=0.010). SD1/SD2 (0.30±0.11 vs. 0.28±0.07; p=0.605) were not significantly different between groups. Patients with COPD presented a visual analysis of Poincaré plot of lower dispersion of RR intervals both beat to beat and the long term.

CONCLUSION

Subjects with COPD present reduction of geometric indexes of HRV, indicating reduced heart rate variability.

Gender-dependent impact of major depression on autonomic cardiovascular modulation

Depression has been shown to increase the risk for cardiovascular disease (CVD) more strongly in women than in men. Although the underlying mechanisms are unknown, a putative role of increased sympathetic modulation has been suggested for the association of CVD and depression. The aim of this study was to investigate possible gender-associated differences of autonomic function in healthy volunteers and patients suffering from major depressive disorder (MDD). Linear as well as non-linear measures of heart rate variability (HRV), blood pressure variability (BPV) and baroreflex sensitivity (BRS) were obtained in each 18 male and 18 female unmedicated patients and respective control subjects. Gender differences were detectable in healthy subjects showing predominant sympathetic modulation in males. This was most obvious in BPV analysis. These gender differences were abolished in patients suffering from MDD, mainly due to altered autonomic modulation in female patients. Our results indicate that BPV is more sensitive to reveal depression-associated changes of autonomic function as compared to HRV. Moreover, female patients contribute most to the overall difference between patients and controls. The shift in the balance of autonomic function in women might account for the increased prevalence of CVD in these patients.

Electrocardiologic and related methods of non-invasive detection and risk stratification in myocardial ischemia: state of the art and perspectives

Background: Electrocardiographic methods still provide the bulk of cardiovascular diagnostics. Cardiac ischemia is associated with typical alterations in cardiac biosignals that have to be measured, analyzed by mathematical algorithms and allegorized for further clinical diagnostics. The fast growing fields of biomedical engineering and applied sciences are intensely focused on generating new approaches to cardiac biosignal analysis for diagnosis and risk stratification in myocardial ischemia.

Objectives: To present and review the state of the art in and new approaches to electrocardiologic methods for non-invasive detection and risk stratification in coronary artery disease (CAD) and myocardial ischemia; secondarily, to explore the future perspectives of these methods.

Methods: In follow-up to the Expert Discussion at the 2008 Workshop on "Biosignal Analysis" of the German Society of Biomedical Engineering in Potsdam, Germany, we comprehensively searched the pertinent literature and databases and compiled the results into this review. Then, we categorized the state-of-the-art methods and selected new approaches based on their applications in detection and risk stratification of myocardial ischemia. Finally, we compared the pros and cons of the methods and explored their future potentials for cardiology.

Results: Resting ECG, particularly suited for detecting ST-elevation myocardial infarctions, and exercise ECG, for the diagnosis of stable CAD, are state-of-the-art methods. New exercise-free methods for detecting stable CAD include cardiogoniometry (CGM); methods for detecting acute coronary syndrome without ST elevation are Body Surface Potential Mapping, functional imaging and CGM. Heart rate variability and blood pressure variability analyses, microvolt T-wave alternans and signal-averaged ECG mainly serve in detecting and stratifying the risk for lethal arrythmias in patients with myocardial ischemia or previous myocardial infarctions. Telemedicine and ambient-assisted living support the electrocardiological monitoring of at-risk patients.

Conclusions: There are many promising methods for the exercise-free, non-invasive detection of CAD and myocardial ischemia in the stable and acute phases. In the coming years, these new methods will help enhance state-of-the-art procedures in routine diagnostics. The future can expect that equally novel methods for risk stratification and telemedicine will transition into clinical routine.

From 'omics' to complex disease: a systems biology approach to gene-environment interactions in cancer

Background

Cancer is a complex disease that involves a sequence of gene-environment interactions in a progressive process that cannot occur without dysfunction in multiple systems, including DNA repair, apoptotic and immune functions. Epigenetic mechanisms, responding to numerous internal and external cues in a dynamic ongoing exchange, play a key role in mediating environmental influences on gene expression and tumor development.

Hypothesis

The hypothesis put forth in this paper addresses the limited success of treatment outcomes in clinical oncology. It states that improvement in treatment efficacy requires a new paradigm that focuses on reversing systemic dysfunction and tailoring treatments to specific stages in the process. It requires moving from a reductionist framework of seeking to destroy aberrant cells and pathways to a transdisciplinary systems biology approach aimed at reversing multiple levels of dysfunction.

Conclusion

Because there are many biological pathways and multiple epigenetic influences working simultaneously in the expression of cancer phenotypes, studying individual components in isolation does not allow an adequate understanding of phenotypic expression. A systems biology approach using new modeling techniques and nonlinear mathematics is needed to investigate gene-environment interactions and improve treatment efficacy. A broader array of study designs will also be required, including prospective molecular epidemiology, immune competent animal models and in vitro/in vivo translational research that more accurately reflects the complex process of tumor initiation and progression.

Complexity of the short-term heart-rate variability

This work has proposed a methodology based on the concept of entropy rates to study the complexity of the short-term heart-rate variability (HRV) for improving risk stratification to predict sudden cardiac death (SCD) of patients with established ischemic-dilated cardiomyopathy (IDC). The short-term HRV was analyzed during daytime and nighttime by means of RR series. An entropy rate was calculated on the RR series, previously transformed to symbol sequences by means of an alphabet. A statistical analysis permitted to stratify high- and low-risk patients of suffering SCD, with a specificity (SP) of 95% and sensitivity (SE) of 83.3%.

Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using fMRI: adapting methods optimized for characterizing autonomic dysregulation to neural dynamic time series

In a well-regulated control system, excitatory and inhibitory components work closely together with minimum lag; in response to inputs of finite duration, outputs should show rapid rise and, following the input's termination, immediate return to baseline. The efficiency of this response can be quantified using the power spectrum density's scaling parameter beta, a measure of self-similarity, applied to the first derivative of the raw signal. In this study, we adapted power spectrum density methods, previously used to quantify autonomic dysregulation (heart rate variability), to neural time series obtained via functional MRI. The negative feedback loop we investigated was the limbic system, using affect-valent faces as stimuli. We hypothesized that trait anxiety would be related to efficiency of regulation of limbic responses, as quantified by power-law scaling of fMRI time series. Our results supported this hypothesis, showing moderate to strong correlations of trait anxiety and beta (r=0.45-0.54) for the amygdala, orbitofrontal cortex, hippocampus, superior temporal gyrus, posterior insula, and anterior cingulate. Strong anticorrelations were also found between the amygdala's beta and wake heart rate variability (r=-0.61), suggesting a robust relationship between dysregulated limbic outputs and their autonomic consequences.

Comparison of linear-stochastic and nonlinear-deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death

Objective:

Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD).

Background:

Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data.

Methods:

Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle–Thaler criteria.

Results:

All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p ≤0.001). The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p ≤0.001); sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p ≤0.001).

Conclusions:

Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test.

Complexity of cardiovascular regulation in small animals

Oscillations of heart rate and blood pressure are related to the activity of the underlying control mechanism. They have been investigated mostly with linear methods in the time and frequency domains. Also, in recent years, many different nonlinear analysis methods have been applied for the evaluation of cardiovascular variability. This review presents the most commonly used nonlinear methods. Physiological understanding is obtained from various results from small animals.

Heart rate variability analysis based on time-frequency representation and entropies in hypertrophic cardiomyopathy patients

In hypertrophic cardiomyopathy (HCM) patients there is an increased risk of premature death, which can occur with little or no warning. Furthermore, classification for sudden cardiac death on patients with HCM is very difficult. The aim of our study was to improve the prognostic value of heart rate variability (HRV) in HCM patients, giving insight into changes of the autonomic nervous system. In this way, the suitability of linear and nonlinear measures was studied to assess the HRV. These measures were based on time-frequency representation (TFR) and on Shannon and Rényi entropies, and compared with traditional HRV measures. Holter recordings of 64 patients with HCM and 55 healthy subjects were analyzed. The HCM patients consisted of two groups: 13 high risk patients, after aborted sudden cardiac death (SCD); 51 low risk patients, without SCD. Five-hour RR signals, corresponding to the sleep period of the subjects, were considered for the analysis as a comparable standard situation. These RR signals were filtered in the three frequency bands: very low frequency band (VLF, 0-0.04 Hz), low frequency band (LF, 0.04-0.15 Hz) and high frequency band (HF, 0.15-0.45 Hz). TFR variables based on instantaneous frequency and energy functions were able to classify HCM patients and healthy subjects (control group). Results revealed that measures obtained from TFR analysis of the HRV better classified the groups of subjects than traditional HRV parameters. However, results showed that nonlinear measures improved group classification. It was observed that entropies calculated in the HF band showed the highest statistically significant levels comparing the HCM group and the control group, p-value < 0.0005. The values of entropy measures calculated in the HCM group presented lower values, indicating a decreasing of complexity, than those calculated from the control group. Moreover, similar behavior was observed comparing high and low risk of premature death, the values of the entropy being lower in high risk patients, p-value < 0.05, indicating an increase of predictability. Furthermore, measures from information entropy, but not from TFR, seem to be useful for enhanced risk stratification in HCM patients with an increased risk of sudden cardiac death.

Introduction: Cardiovascular physics

The number of patients suffering from cardiovascular diseases increases unproportionally high with the increase of the human population and aging, leading to very high expenses in the public health system. Therefore, the challenge of cardiovascular physics is to develop high-sophisticated methods which are able to, on the one hand, supplement and replace expensive medical devices and, on the other hand, improve the medical diagnostics with decreasing the patient's risk. Cardiovascular physics-which interconnects medicine, physics, biology, engineering, and mathematics-is based on interdisciplinary collaboration of specialists from the above scientific fields and attempts to gain deeper insights into pathophysiology and treatment options. This paper summarizes advances in cardiovascular physics with emphasis on a workshop held in Bad Honnef, Germany, in May 2005. The meeting attracted an interdisciplinary audience and led to a number of papers covering the main research fields of cardiovascular physics, including data analysis, modeling, and medical application. The variety of problems addressed by this issue underlines the complexity of the cardiovascular system. It could be demonstrated in this Focus Issue, that data analyses and modeling methods from cardiovascular physics have the ability to lead to significant improvements in different medical fields. Consequently, this Focus Issue of Chaos is a status report that may invite all interested readers to join the community and find competent discussion and cooperation partners.