Respiratory Sinus Arrhythmia Mechanisms in Young Obese Subjects

Autonomic nervous system (ANS) activity and imbalance between its sympathetic and parasympathetic components are important factors contributing to the initiation and progression of many cardiovascular disorders related to obesity. The results on respiratory sinus arrhythmia (RSA) magnitude changes as a parasympathetic index were not straightforward in previous studies on young obese subjects. Considering the potentially unbalanced ANS regulation with impaired parasympathetic control in obese patients, the aim of this study was to compare the relative contribution of baroreflex and non-baroreflex (central) mechanisms to the origin of RSA in obese vs. control subjects. To this end, we applied a recently proposed information-theoretic methodology – partial information decomposition (PID) – to the time series of heart rate variability (HRV, computed from RR intervals in the ECG), systolic blood pressure (SBP) variability, and respiration (RESP) pattern measured in 29 obese and 29 ageand gender-matched non-obese adolescents and young adults monitored in the resting supine position and during postural and cognitive stress evoked by head-up tilt and mental arithmetic. PID was used to quantify the so-called unique information transferred from RESP to HRV and from SBP to HRV, reflecting, respectively, non-baroreflex and RESP-unrelated baroreflex HRV mechanisms, and the redundant information transferred from (RESP, SBP) to HRV, reflecting RESP-related baroreflex RSA mechanisms. Our results suggest that obesity is associated: (i) with blunted involvement of non-baroreflex RSA mechanisms, documented by the lower unique information transferred from RESP to HRV at rest; and (ii) with a reduced response to postural stress (but not to mental stress), documented by the lack of changes in the unique information transferred from RESP and SBP to HRV in obese subjects moving from supine to upright, and by a decreased redundant information transfer in obese compared to controls in the upright position. These findings were observed in the presence of an unchanged RSA magnitude measured as the high frequency (HF) power of HRV, thus suggesting that the changes in ANS imbalance related to obesity in adolescents and young adults are subtle and can be revealed by dissecting RSA mechanisms into its components during various challenges

Non-Linear Heart Rate Variability and Risk Stratification in Cardiovascular Disease

Traditional time and frequency domain heart rate variability (HRV) have cardiac patients at risk of mortality post-myocardial infarction. More recently, non linear HRV has been applied to risk stratification of cardiac patients. In this review we describe studies of non linear HRV and outcome in cardiac patients. We have included studies that used the three most common non-linear indices: power law slope, the short term fractal scaling exponent and measures based on Poincaré plots. We suggest that a combination of traditional and non-linear HRV may be optimal for risk stratification. Considerations in using non linear HRV in a clinical setting are described

Cardiac Health Diagnosis Using Higher Order Spectra and Support Vector Machine

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification

Nonlinear trend removal should be carefully performed in heart rate variability analysis

$\bullet$ Background : In Heart rate variability analysis, the rate-rate time series suffer often from aperiodic non-stationarity, presence of ectopic beats etc. It would be hard to extract helpful information from the original signals. 10 $\bullet$ Problem : Trend removal methods are commonly practiced to reduce the influence of the low frequency and aperiodic non-stationary in RR data. This can unfortunately affect the signal and make the analysis on detrended data less appropriate. $\bullet$ Objective : Investigate the detrending effect (linear \& nonlinear) in temporal / nonliear analysis of heart rate variability of long-term RR data (in normal sinus rhythm, atrial fibrillation, 15 congestive heart failure and ventricular premature arrhythmia conditions). $\bullet$ Methods : Temporal method : standard measure SDNN; Nonlinear methods : multi-scale Fractal Dimension (FD), Detrended Fluctuation Analysis (DFA) \& Sample Entropy (Sam-pEn) analysis. $\bullet$ Results : The linear detrending affects little the global characteristics of the RR data, either 20 in temporal analysis or in nonlinear complexity analysis. After linear detrending, the SDNNs are just slightly shifted and all distributions are well preserved. The cross-scale complexity remained almost the same as the ones for original RR data or correlated. Nonlinear detrending changed not only the SDNNs distribution, but also the order among different types of RR data. After this processing, the SDNN became indistinguishable be-25 tween SDNN for normal sinus rhythm and ventricular premature beats. Different RR data has different complexity signature. Nonlinear detrending made the all RR data to be similar , in terms of complexity. It is thus impossible to distinguish them. The FD showed that nonlinearly detrended RR data has a dimension close to 2, the exponent from DFA is close to zero and SampEn is larger than 1.5 -- these complexity values are very close to those for 30 random signal. $\bullet$ Conclusions : Pre-processing by linear detrending can be performed on RR data, which has little influence on the corresponding analysis. Nonlinear detrending could be harmful and it is not advisable to use this type of pre-processing. Exceptions do exist, but only combined with other appropriate techniques to avoid complete change of the signal's intrinsic dynamics. 35 Keywords $\bullet$ heart rate variability $\bullet$ linear / nonlinear detrending $\bullet$ complexity analysis $\bullet$ mul-tiscale analysis $\bullet$ detrended fluctuation analysis $\bullet$ fractal dimension $\bullet$ sample entropy

Respiratory sinus arrhythmia quantified with linear and non-linear techniques to classify dilated and ischemic cardiomyopathy

Peer ReviewedPostprint (author's final draft

Association between anxiety disorders and heart rate variability in The Netherlands Study of Depression and Anxiety (NESDA)

Objective: To determine whether patients with different types of anxiety disorder (panic disorder, social phobia, generalized anxiety disorder) have higher heart rate and lower heart rate variability compared with healthy controls in a sample that was sufficiently powered to examine the confounding effects of lifestyle and antidepressants. Methods: The standard deviation of the normal-to-normal intervals (SDNN), heart rate (HR), and respiratory sinus arrhythmia (RSA) were measured in 2059 subjects (mean age = 41.7 years, 66.8% female) participating in The Netherlands Study of Depression and Anxiety (NESDA). Based on the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) and Composite International Diagnostic Interview (CIDI), NESDA participants were classified as healthy controls (n = 616), subjects with an anxiety diagnosis earlier in life (n = 420), and Subjects with current anxiety diagnosis (n = 1059). Results: Current anxious Subjects had a significantly lower SDNN and RSA compared with controls. RSA was also significantly lower in remitted anxious subjects compared with controls. These associations were similar across the three different types of anxiety disorders. Adjustment for lifestyle had little impact. However, additional adjustment for antidepressant use reduced all significant associations between anxiety and HRV to nonsignificant. Anxious subjects who used a tricyclic antidepressant, it selective serotonin reuptake inhibitor, or another antidepressant showed significantly lower mean SDNN and RSA compared with controls (effect sizes = 0.20-0.80 for SDNN and 0.42-0.79 for RSA). Nonmedicated anxious subjects did not differ from controls in mean SDNN and RSA. Conclusion: This study shows that anxiety disorders are associated with significantly lower HR variability, but the association seems to be driven by the effects of antidepressants

Neurophysiological Assessment of Affective Experience

In the field of Affective Computing the affective experience (AX) of the user during the interaction with computers is of great interest. The automatic recognition of the affective state, or emotion, of the user is one of the big challenges. In this proposal I focus on the affect recognition via physiological and neurophysiological signals. Long‐standing evidence from psychophysiological research and more recently from research in affective neuroscience suggests that both, body and brain physiology, are able to indicate the current affective state of a subject. However, regarding the classification of AX several questions are still unanswered. The principal possibility of AX classification was repeatedly shown, but its generalisation over different task contexts, elicitating stimuli modalities, subjects or time is seldom addressed. In this proposal I will discuss a possible agenda for the further exploration of physiological and neurophysiological correlates of AX over different elicitation modalities and task contexts