Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics

Nonlinear digital signal processing methods that address system complexity have provided useful computational tools for helping in the diagnosis and treatment of a wide range of pathologies. More specifically, nonlinear measures have been successful in characterizing patients with mental disorders such as Major Depression (MD). In this study, we propose the use of instantaneous measures of entropy, namely the inhomogeneous point-process approximate entropy (ipApEn) and the inhomogeneous point-process sample entropy (ipSampEn), to describe a novel characterization of MD patients undergoing affective elicitation. Because these measures are built within a nonlinear point-process model, they allow for the assessment of complexity in cardiovascular dynamics at each moment in time. Heartbeat dynamics were characterized from 48 healthy controls and 48 patients with MD while emotionally elicited through either neutral or arousing audiovisual stimuli. Experimental results coming from the arousing tasks show that ipApEn measures are able to instantaneously track heartbeat complexity as well as discern between healthy subjects and MD patients. Conversely, standard heart rate variability (HRV) analysis performed in both time and frequency domains did not show any statistical significance. We conclude that measures of entropy based on nonlinear point-process models might contribute to devising useful computational tools for care in mental health

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

The Effect of Visual Experience on the Development of Functional Architecture in hMT+

We investigated whether the visual hMT+ cortex plays a role in supramodal representation of sensory flow, not mediated by visual mental imagery. We used functional magnetic resonance imaging to measure neural activity in sighted and congenitally blind individuals during passive perception of optic and tactile flows. Visual motion-responsive cortex, including hMT+, was identified in the lateral occipital and inferior temporal cortices of the sighted subjects by response to optic flow. Tactile flow perception in sighted subjects activated the more anterior part of these cortical regions but deactivated the more posterior part. By contrast, perception of tactile flow in blind subjects activated the full extent, including the more posterior part. These results demonstrate that activation of hMT+ and surrounding cortex by tactile flow is not mediated by visual mental imagery and that the functional organization of hMT+ can develop to subserve tactile flow perception in the absence of any visual experience. Moreover, visual experience leads to a segregation of the motion-responsive occipitotemporal cortex into an anterior subregion involved in the representation of both optic and tactile flows and a posterior subregion that processes optic flow only

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

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