Mimma Nardelli

Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis.

Current clinical practice in diagnosing patients affected by psychiatric disorders such as bipolar disorder is based only on verbal interviews and scores from specific questionnaires, and no reliable and objective psycho-physiological markers are taken into account. In this paper, we propose to use a wearable system based on a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire electrocardiogram, respirogram, and body posture information in order to detect a pattern of objective physiological parameters to support diagnosis. Moreover, we implemented a novel ad hoc methodology of advanced biosignal processing able to effectively recognize four possible clinical mood states in bipolar patients (i.e., depression, mixed state, hypomania, and euthymia) continuously monitored up to 18 h, using heart rate variability information exclusively. Mood assessment is intended as an intrasubject evaluation in which the patients states are modeled as a Markov chain, i.e., in the time domain, each mood state refers to the previous one. As validation, eight bipolar patients were monitored collecting and analyzing more than 400 h of autonomic and cardiovascular activity. Experimental results demonstrate that our novel concept of personalized and pervasive monitoring constitutes a viable and robust clinical decision support system for bipolar disorders recognizing mood states with a total classification accuracy up to 95.81%.

Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability

This paper reports on how emotional states elicited by affective sounds can be effectively recognized by means of estimates of Autonomic Nervous System (ANS) dynamics. Specifically, emotional states are modeled as a combination of arousal and valence dimensions according to the well-known circumplex model of affect, whereas the ANS dynamics is estimated through standard and nonlinear analysis of Heart rate variability (HRV) exclusively, which is derived from the electrocardiogram (ECG). In addition, Lagged Poincaré Plots of the HRV series were also taken into account. The affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal (intensity) and two levels of valence (unpleasant and pleasant). A group of 27 healthy volunteers were administered with these standardized stimuli while ECG signals were continuously recorded. Then, those HRV features showing significant changes (p

Mood states modulate complexity in heartbeat dynamics: A multiscale entropy analysis

<;

Design and preliminary affective characterization of a novel fabric-based tactile display

0.05 from statistical tests) between the arousal and valence dimensions were used as input of an automatic classification system for the recognition of the four classes of arousal and two classes of valence. Experimental results demonstrated that a quadratic discriminant classifier, tested through Leave-One-Subject-Out procedure, was able to achieve a recognition accuracy of 84.72 percent on the valence dimension, and 84.26 percent on the arousal dimension.

Predicting Mood Changes in Bipolar Disorder Through Heartbeat Nonlinear Dynamics

This paper demonstrates that heartbeat complex dynamics is modulated by different pathological mental states. Multiscale entropy analysis was performed on R-R interval series gathered from the electrocardiogram of eight bipolar patients who exhibited mood states among depression, hypomania, and euthymia, i.e., good affective balance. Three different methodologies for the choice of the sample entropy radius value were also compared. We show that the complexity level can be used as a marker of mental states being able to discriminate among the three pathological mood states, suggesting to use heartbeat complexity as a more objective clinical biomarker for mental disorders.

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

In this work we present a novel wearable haptic system based on an elastic fabric which can be moved forward and backward over the user forearm thus simulating a human caress. The system allows to control both the velocity of the “caress-like” movement, by regulating motor velocity, and the “strength of the caress”, by regulating motor positions and hence the force exerted by the fabric on the user forearm. Along with a description of the mechanical design and control of the system, we also report the preliminary results of psycho-physiological assessment tests performed by six healthy participants. Such an assessment is intended as a preliminary characterization of the device capability of eliciting tactually emotional states in humans using different combinations of velocity and caress strength. The emotional state is expressed in terms of arousal and valence. Moreover, the activation of the autonomic nervous system is also evaluated through the analysis of the electrodermal response (EDR). The main results reveal a statistically significant correlation between the perceived arousal level and the “strength of the caress” and between the perceived valence level and the “velocity of the caress”. Moreover, we found that phasic EDR is able to discern between pleasant and unpleasant stimuli. These preliminary results are very encouraging and confirm the effectiveness of this device in conveying emotional-like haptic stimuli in a controllable and wearable fashion.

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

Bipolar disorder (BD) is characterized by an alternation of mood states from depression to (hypo)mania. Mixed states, i.e., a combination of depression and mania symptoms at the same time, can also be present. The diagnosis of this disorder in the current clinical practice is based only on subjective interviews and questionnaires, while no reliable objective psycho-physiological markers are available. Furthermore, there are no biological markers predicting BD outcomes, or providing information about the future clinical course of the phenomenon. To overcome this limitation, here we propose a methodology predicting mood changes in BD using heartbeat nonlinear dynamics exclusively, derived from the ECG. Mood changes are here intended as transitioning between two mental states: euthymic state (EUT), i.e., the good affective balance, and non-euthymic (non-EUT) states. Heart rate variability (HRV) series from 14 bipolar spectrum patients (age: 33.43 ± 9.76, age range: 23-54; six females) involved in the European project PSYCHE, undergoing whole night electrocardiogram (ECG) monitoring were analyzed. Data were gathered from a wearable system comprised of a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire ECGs. Each patient was monitored twice a week, for 14 weeks, being able to perform normal (unstructured) activities. From each acquisition, the longest artifact-free segment of heartbeat dynamics was selected for further analyses. Sub-segments of 5 min of this segment were used to estimate trends of HRV linear and nonlinear dynamics. Considering data from a current observation at day t0, and past observations at days (t-1, t-2,...,), personalized prediction accuracies in forecasting a mood state (EUT/non-EUT) at day t+1 were 69% on average, reaching values as high as 83.3%. This approach opens to the possibility of predicting mood states in bipolar patients through heartbeat nonlinear dynamics exclusively.

Cross-lattice behavior of general ACO folding for proteins in the HP model

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.

Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions.

Electrodermal activity analysis during affective haptic elicitation.

The computational investigation of protein folding is one of the most relevant challenges in bioinformatics. In this field, simplified lattice models for proteins like the classical HP model have been proposed, and different lattice types can be employed. A promising approach to find ground state conformations relies on Ant Colony Optimization (ACO), a popular biology-inspired heuristics: several variants have been implemented so far, on square lattices in 2D and 3D. In this paper we propose a general scheme of ACO for HP on both square and triangular lattices in 2D and 3D, including also a novel initialization procedure for the pheromone matrix according to some pre-computed suboptimal conformations. The algorithm behavior, considering the influence of the optional parts and the required parameter tuning, is investigated for the first time with experiments that systematically span different lattice types. The test outcomes are useful in understanding how to operate on the algorithm parameters. The presented results are used to sketch out general guidelines for the practical employment of ACO in conformational studies, depending on the chosen sequences and lattice types.