Mind-body relationships in elite apnea divers during breath holding: a study of autonomic responses to acute hypoxemia

The mental control of ventilation with all associated phenomena, from relaxation to modulation of emotions, from cardiovascular to metabolic adaptations, constitutes a psychophysiological condition characterizing voluntary breath-holding (BH). BH induces several autonomic responses, involving both autonomic cardiovascular and cutaneous pathways, whose characterization is the main aim of this study. Electrocardiogram and skin conductance (SC) recordings were collected from 14 elite divers during three conditions: free breathing (FB), normoxic phase of BH (NPBH) and hypoxic phase of BH (HPBH). Thus, we compared a set of features describing signal dynamics between the three experimental conditions: from heart rate variability (HRV) features (in time and frequency-domains and by using nonlinear methods) to rate and shape of spontaneous SC responses (SCRs). The main result of the study rises by applying a Factor Analysis to the subset of features significantly changed in the two BH phases. Indeed, the Factor Analysis allowed to uncover the structure of latent factors which modeled the autonomic response: a factor describing the autonomic balance (AB), one the information increase rate (IIR), and a latter the central nervous system driver (CNSD). The BH did not disrupt the FB factorial structure, and only few features moved among factors. Factor Analysis indicates that during BH (1) only the SC described the emotional output, (2) the sympathetic tone on heart did not change, (3) the dynamics of interbeats intervals showed an increase of long-range correlation that anticipates the HPBH, followed by a drop to a random behavior. In conclusion, data show that the autonomic control on heart rate and SC are differentially modulated during BH, which could be related to a more pronounced effect on emotional control induced by the mental training to BH

Effect of mechanical preconditioning on the electrical properties of knitted conductive textiles during cyclic loading

This paper presents, for the first time, the electrical response of knitted conductive fabrics to a considerable number of cycles of deformation in view of their use as wearable sensors. The changes in the electrical properties of four knitted conductive textiles, made of 20% stainless steel and 80% polyester fibers, were studied during unidirectional elongation in an Instron machine. Two tests sessions of 250 stretch–recovery cycles were conducted for each sample at two elongation rates (9.6 and 12 mm/s) and at three constant currents (1, 3 and 6 mA). The first session assessed the effects of an extended cyclic mechanical loading (preconditioning) on the electrical properties, especially on the electrical stabilization. The second session, which followed after a 5 minute interval under identical conditions, investigated whether the stabilization and repeatability of the electrical features were maintained after rest. The influence of current and elongation rate on the resistance measurements was also analyzed. In particular, the presence of a semiconducting behavior of the stainless steel fibers was proved by means of different test currents. Lastly, the article shows the time-dependence of the fabrics by means of hysteresis graphs and their non-linear behavior thanks to a time–frequency analysis. All knit patterns exhibited interesting changes in electrical properties as a result of mechanical preconditioning and extended use. For instance, the gauge factor, which indicates the sensitivity of the fabric sensor, varied considerably with the number of cycles, being up to 20 times smaller than that measured using low cycle number protocols

Instantaneous Assessment of Hedonic Olfactory Perception using Heartbeat Nonlinear Dynamics: A Preliminary Study

Emotional perception to hedonic olfactory stimuli is un- der direct control of the limbic system, whose dynam- ics is known to affect autonomic nervous system activity on cardiovascular control. Mainly due to methodologi- cal limitations, previous investigations failed to uncover specific trends in heartbeat dynamics between ultra short- time (i.e., lasting < 10s), pleasant and unpleasant olfac- tory stimuli. To this extent, in this study we computed in- stantaneous estimates from heartbeat series gathered from 32 healthy subjects (age: 26±2; 16M) undergoing hedo- nic olfactory elicitation. Each subject exhibited a simi- lar olfactory perception threshold, and scored five 5s stim- uli in terms of arousal and valence level using the self- assessment manikin test. We analyzed the heartbeat series using our recently proposed inhomogeneous point-process nonlinear framework, obtaining instantaneous estimates defined in the time (mean and standard deviation), fre- quency (power in the LF and HF bands, as well as LF/HF ratio), and nonlinear/complexity (bispectra, sample and approximate entropy, and Lyapunov exponent) domains. A feature set comprising average estimates within the 5s win- dows was taken as an input of a K-Nearest Neighborhood classification algorithm, whose cross-validation relied on a leave-one-subject-out procedure. Results demonstrate that our framework allows to finely characterize affective olfactory elicitation with an average recognition accuracy of 71.88%. Feature selection highlighted that the most dis- criminating power was contributed by instantaneous LF power, instantaneous Lyapunov exponents, and instanta- neous approximate entropy

Force-Velocity Assessment of Caress-Like Stimuli Through the Electrodermal Activity Processing: Advantages of a Convex Optimization Approach

We propose the use of the convex optimization-based EDA (cvxEDA) framework to automatically characterize the force and velocity of caressing stimuli through the analysis of the electrodermal activity (EDA). CvxEDA, in fact, solves a convex optimization problem that always guarantees the globally optimal solution. We show that this approach is especially suitable for the implementation in wearable monitoring systems, being more computationally efficient than a widely used EDA processing algorithm. In addition, it ensures low-memory consumption, due to a sparse representation of the EDA phasic components. EDA recordings were gathered from 32 healthy subjects (16 females) who participated in an experiment where a fabric-based wearable haptic system conveyed them caress-like stimuli by means of two motors. Six types of stimuli (combining three levels of velocity and two of force) were randomly administered over time. Performance was evaluated in terms of execution time of the algorithm, memory usage, and statistical significance in discerning the affective stimuli along force and velocity dimensions. Experimental results revealed good performance of cvxEDA model for all of the considered metrics

Muscle activity and inactivity periods during normal daily life

Recent findings suggest that not only the lack of physical activity, but also prolonged times of sedentary behaviour where major locomotor muscles are inactive, significantly increase the risk of chronic diseases. The purpose of this study was to provide details of quadriceps and hamstring muscle inactivity and activity during normal daily life of ordinary people. Eighty-four volunteers (44 females, 40 males, 44.1&plusmn;17.3 years, 172.3&plusmn;6.1 cm, 70.1&plusmn;10.2 kg) were measured during normal daily life using shorts measuring muscle electromyographic (EMG) activity (recording time 11.3&plusmn;2.0 hours). EMG was normalized to isometric MVC (EMGMVC) during knee flexion and extension, and inactivity threshold of each muscle group was defined as 90% of EMG activity during standing (2.5&plusmn;1.7% of EMGMVC). During normal daily life the average EMG amplitude was 4.0&plusmn;2.6% and average activity burst amplitude was 5.8&plusmn;3.4% of EMGMVC (mean duration of 1.4&plusmn;1.4 s) which is below the EMG level required for walking (5 km/h corresponding to EMG level of about 10% of EMGMVC). Using the proposed individual inactivity threshold, thigh muscles were inactive 67.5&plusmn;11.9% of the total recording time and the longest inactivity periods lasted for 13.9&plusmn;7.3 min (2.5&ndash;38.3 min). Women had more activity bursts and spent more time at intensities above 40% EMGMVC than men (p&lt;0.05). In conclusion, during normal daily life the locomotor muscles are inactive about 7.5 hours, and only a small fraction of muscle\u27s maximal voluntary activation capacity is used averaging only 4% of the maximal recruitment of the thigh muscles. Some daily non-exercise activities such as stair climbing produce much higher muscle activity levels than brisk walking, and replacing sitting by standing can considerably increase cumulative daily muscle activity

Gaussian processes with physiologically-inspired priors for physical arousal recognition

While machine learning algorithms are able to detect subtle patterns of interest in data, expert knowledge may contain crucial information that is not easily extracted from a given dataset, especially when the latter is small or noisy. In this paper we investigate the suitability of Gaussian Process Classification (GPC) as an effective model to implement the domain knowledge in an algorithm’s training phase. Building on their Bayesian nature, we proceed by injecting problemspecific domain knowledge in the form of an a-priori distribution on the GPC latent function. We do this by extracting handcrafted features from the input data, and correlating them to the logits of the classification problem through fitting a prior function informed by the physiology of the problem. The physiologically-informed prior of the GPC is then updated through the Bayes formula using the available dataset. We apply the methods discussed here to a two-class classification problem associated to a dataset comprising Heart Rate Variability (HRV) and Electrodermal Activity (EDA) signals collected from 26 subjects who were exposed to a physical stressor aimed at altering their autonomic nervous systems dynamics. We provide comparative computational experiments on the selection of appropriate physiologically-inspired GPC prior functions. We find that the recognition of the presence of the physical stressor is significantly enhanced when the physiologically-inspired prior knowledge is injected into the GPC model

Assessment of dynamic autonomic changes with posture using instantaneous entropy measures

Dynamic analysis provides a powerful methodological framework for characterizing physiological systems. In particular, complex heartbeat dynamics related to autonomic control mechanisms are known to change at each moment in time, and complexity measures have been proven to have prognostic value in both health and disease. Nevertheless, an instantaneous measure of complexity for cardiovascular time series (or any other series of stochastic physiological "events ") is still missing. In this study we introduce a mathematical framework serving instantaneous complex estimates of heartbeat dynamics to characterize different activities, tasks, and/or pathological states. In particular we propose new definitions of inhomogeneous point-process approximate and sample entropy where the discrete events are modeled by probability density functions characterizing and predicting the time until the next event occurs as a function of past history. These definitions are built on our previous work employing Laguerre expansions of the Wiener-Volterra autoregressive terms to account for long-term memory. We demonstrate an exemplary study on heartbeat data gathered from healthy subjects undergoing postural changes such as stand-up, slow tilt, and fast tilt. Results show that instantaneous complexity is able to effectively track the complex autonomic changes as they are affected by different postural changes

Instantaneous Bispectral Characterization of the Autonomic Nervous System through a Point-Process Nonlinear Model

Assessment of Heartbeat nonlinear dynamics is an important topic in the study of cardiovascular control physiology. In this paper, we introduce an inverse-Gaussian pointprocess model where an input-output Wiener-Volterra model is linked to a quadratic autoregression within the probability structure in order to estimate the dynamic spectrum and bispectrum of the considered heartbeat dynamics. The proposed framework was tested with an experimental ECG dataset with subjects undergoing a tilt-table procedure. Results show that our model is useful in estimating previously defined instantaneous indices of heart rate (HR) and heart rate variability (HRV). Results demonstrate that the algorithm confirms the characterization of the tilt effect on standard and instantaneous indices of the sympatho-vagal balance, while simultaneously tracking significant changes in the inherent nonlinearity of heartbeat dynamics with tilt. © 2013 Springer-Verlag