Application of time series spectral analysis theory: analysis of cardiovascular variability signals

The paper focuses on the most important application problems commonly encountered in spectral analysis of short-term (less than 10 min) recordings of cardiovascular variability signals (CVSs), critically analysing the different approaches to these problems presented in the literature and suggesting practical solutions based on sound theoretical and empirical considerations. The Blackman-Tukey (BT) and Burg methods have been selected as the most representative of classical and AR spectral estimators, respectively. For realistic simulations, ‘synthetic’ CVSs are generated as AR processes whose parameters are estimated on corresponding time series of normal, post-myocardial infarction and congestive heart failure subjects. The problem of resolution of spectral estimates is addressed, and an empirical method is proposed for model order selection in AR estimation. The issue of the understandability and interpretability of spectral shapes is discussed. The problem of non-stationarity and removing trends is dealt with. The important issue of identification and estimation of spectral components is discussed, and the main advantages and drawbacks of spectral decomposition algorithms are critically evaluated.     

Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability

A nonlinear prediction method for investigating the dynamic interdependence between short length time series is presented. The method is a generalization to bivariate prediction of the univariate approach based on nearest neighbor local linear approximation. Given the input and output series x and y, the relationship between a pattern of samples of x and a synchronous sample of y was approximated with a linear polynomial whose coefficients were estimated from an equation system including the nearest neighbor patterns in x and the corresponding samples in y. To avoid overfitting and waste of data, the training and testing stages of the prediction were designed through a specific out-of-sample cross validation. The robustness of the method was assessed on short realizations of simulated processes interacting either linearly or nonlinearly. The predictor was then used to characterize the dynamical interaction between the short-term spontaneous fluctuations of heart period (RR interval) and systolic arterial pressure (SAP) in healthy young subjects. In the supine position, the predictability of RR given SAP was low and influenced by nonlinear dynamics. After head-up tilt the predictability increased significantly and was mostly due to linear dynamics. These findings were related to the larger involvement of the baroreflex regulation from SAP to RR in upright than in supine humans, and to the simplification of the RR–SAP coupling occurring with the tilt-induced alteration of the neural regulation of the cardiovascular rhythms.     

The relationship between mental and physical health: Insights from the study of heart rate variability

Here we review our recent body of work on the impact of mood and comorbid anxiety disorders, alcohol dependence, and their treatments on heart rate variability (HRV), a psychophysiological marker of mental and physical wellbeing. We have shown that otherwise healthy, unmedicated patients with these disorders display reduced resting-state HRV, and that pharmacological treatments do not ameliorate these reductions. Other studies highlight that tricyclic medications and the serotonin and noradrenaline reuptake inhibitors in particular may have adverse cardiovascular consequences. Reduced HRV has important functional significance for motivation to engage social situations, social approach behaviours, self-regulation and psychological flexibility in the face of stressors. Over the longer-term, reduced HRV leads to immune dysfunction and inflammation, cardiovascular disease and mortality, attributable to the downstream effects of a poorly functioning cholinergic anti-inflammatory reflex. We place our research in the context of the broader literature base and propose a working model for the effects of mood disorders, comorbid conditions, and their treatments to help guide future research activities. Further research is urgently needed on the long-term effects of autonomic dysregulation in otherwise healthy psychiatric patients, and appropriate interventions to halt the progression of a host of conditions associated with morbidity and mortality.     

Model for the assessment of heart period and arterial pressure variability interactions and of respiration influences

A model which assesses the closed-loop interaction between heart period (HP) and arterial pressure (AP) variabilities and the influence of respiration on both is applied to evaluate the sources of low frequency (LF∼0·1 Hz) and high frequency (HF, respiratory rate ∼0·25 Hz) in conscious dogs (n=18) and humans (n=5). A resonance of AP closed-loop regulation is found to amplify LF oscillations. In dogs, the resonance gain increases slightly during baroreceptor unloading (mild hypotension obtained with nitroglycerine (NTG) i.v. infusion, n=8) and coronary artery occlusion ((CAO), n=6), and it is abolished by ganglionic transmission blockade ((ARF), Arfonad i.v. infusion, n=3). In humans, this gain is considerably increased by passive tilt. Different, possibly central, sources of LF oscillations are also evaluated, finding a strong rhythmic modulation of HP during CAO. At HF, a direct respiratory arrhythmia is dominant in dogs at control, while it is considerably reduced during CAO. On the contrary, in humans, a strong influence of respiration on AP is shown which induces a reflex respiratory arrhythmia. An index of the gain of baroreceptive response, α_cl, was decreased by NTG and CAO, and virtually abolished by chronic arterial baroreceptive denervation (TABD, n=4) and ARF.     

Non-contact monitoring of heart rate variability using medical radar for the evaluation of dynamic changes in autonomic nervous activity during a head-up tilt test

Abstract

Electrocardiography (ECG) is a mandatory standard for monitoring electrical activity of the heart in many clinical settings such as intensive care and emergency units. However, in situations wherein the skin is damaged, such as acute burn injuries, it is impossible to efficiently attach electrodes to the skin. In this study, we developed a non-contact cardiac monitoring system using a 24-GHz medical radar for directly measuring the beat-to-beat heart mechanical activity at a distance from a subject. The heart rate variability (HRV) was analysed using an autoregressive model (AR) from the measured beat-to-beat intervals during a head-up tilt test. To investigate the feasibility of the proposed system, we compared medical radar and ECG recording by using Lin’s correlation coefficient and Bland–Altman analysis, which showed a negligible mean difference from the substantial agreement of Lin’s correlation coefficient of 0.9 between the radar and ECG. The non-contact radar clearly monitored dynamic changes in HRV indices induced by the head-up tilt test. This type of non-contact HRV-sensing technique as an alternative approach has significant potential for advancing personal healthcare in both clinical and out-of-hospital settings.