Vocal features obtained through automated methods in verbal fluency tasks can aid the identification of mixed episodes in bipolar disorder.

There is a lack of consensus on the diagnostic thresholds that could improve the detection accuracy of bipolar mixed episodes in clinical settings. Some studies have shown that voice features could be reliable biomarkers of manic and depressive episodes compared to euthymic states, but none thus far have investigated whether they could aid the distinction between mixed and non-mixed acute bipolar episodes. Here we investigated whether vocal features acquired via verbal fluency tasks could accurately classify mixed states in bipolar disorder using machine learning methods.

Brain Dynamics Induced by Pleasant/Unpleasant Tactile Stimuli Conveyed by Different Fabrics.

In this study, we investigated brain dynamics from electroencephalographic (EEG) signals during affective tactile stimulation conveyed by the dynamical contact with different fabrics. Thirty-three healthy subjects (16 females) were enrolled to interact with a haptic device able to mimic caress-like stimuli conveyed by strips of different fabrics moved back and forth at different velocities. Specifically, two velocity levels (i.

Monitoring voluntary blink magnitude through a wearable eye-tracking system: A preliminary study.

This study proposes a novel approach to measure the contractile force of eye blink that is generally obtained from the orbicularis oculi activity through Ocular ElectroMyo-Graphy (O-EMG). Here, O-EMG is compared with the eye information acquired through a wearable head-mounted eye-tracking system in order to investigate the possibility of using the eye-tracking in place of the O-EMG. Eight subjects were simultaneously monitored through an O-EMG and the eye-tracker while they were performing a structured protocol implying a variation in the blink contractile strength.

Heart rate variability analysis during muscle fatigue due to prolonged isometric contraction.

Fatigue can be defined as the muscular condition occurring before the inability to perform a task. It can be assessed through the evaluation of the median and mean frequency of the spectrum of the surface electromyography series. Previous studies investigated the relationship between heartbeat dynamics and muscular activity.

The role of nonlinear coupling in Human-Horse Interaction: A preliminary study.

This study focuses on the analysis of human-horse dynamic interaction using cardiovascular information exclusively. Specifically, the Information Theoretic Learning (ITL) approach has been applied to a Human-Horse Interaction paradigm, therefore accounting for the nonlinear information of the heart-heart interplay between humans and horses. Heartbeat dynamics was gathered from humans and horses during three experimental conditions: absence of interaction, visual-olfactory interaction, and brooming.

Muscle fatigue assessment through electrodermal activity analysis during isometric contraction.

We studied the effects of muscle fatigue on the Autonomic Nervous System (ANS) dynamics. Specifically, we monitored the electrodermal activity (EDA) on 32 healthy subjects performing isometric biceps contraction. As assessed by means of an electromyography (EMG) analysis, 15 subjects showed muscle fatigue and 17 did not.

Automatic recognition of pleasant content of odours through ElectroEncephaloGraphic activity analysis.

This study presents a machine learning approach applied to ElectroEnchephaloGraphic (EEG) response in a group of subjects when exposed to a controlled olfactory stimulation experiment. In the literature, in fact, there are controversial results on EEG response to odorants. This study proposes a robust leave-one-subject-out classification method to recognize features extracted from EEG signals belonging to pleasant or unpleasant olfactory stimulation classes.

Quantitative heartbeat coupling measures in human-horse interaction.

We present a study focused on a quantitative estimation of a human-horse dynamic interaction. A set of measures based on magnitude and phase coupling between heartbeat dynamics of both humans and horses in three different conditions is reported: no interaction, visual/olfactory interaction and grooming. Specifically, Magnitude Squared Coherence (MSC), Mean Phase Coherence (MPC) and Dynamic Time Warping (DTW) have been used as estimators of the amount of coupling between human and horse through the analysis of their heart rate variability (HRV) time series in a group of eleven human subjects, and one horse.

The Florentine Archives in Transition: Government, Warfare and Communication (1289-1530 ca.).

A turning point in European administrative and documentary practices was traditionally associated, most famously by Robert-Henri Bautier, with the monarchies of the late sixteenth and early seventeenth centuries. By summarizing previous research in this field, as well as by using both published and unpublished sources, this article intends to underline an earlier process of transition connected to the development of significant new techniques for the production and preservation of documents in Renaissance Italian city-states. Focusing on the important case of Florence, the administrative uses of records connected to government, diplomacy and military needs will be discussed, and evidence will be provided that such documentary practices accelerated significantly during the so-called Italian Wars (from 1494 onwards).

A synergy-based hand control is encoded in human motor cortical areas.

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown.

Voice quality in patients suffering from bipolar disease.

People suffering from bipolar disease are more and more common. Such pathology can severely affect patients' lifestyle by wide, and sometimes extreme, mood swings. Biosignals can be very useful to understand this disease.

Removing movement artifacts from equine ECG recordings acquired with textile electrodes.

This study reports on the implementation of a novel system to detect and reduce movement artifact (MA) contribution in electrocardiogram (ECG) recordings acquired from horses in free movement conditions. The system comprises both integrated textile electrodes for ECG acquisition and one triaxial accelerometer for movement monitoring. Here, ECG and physical activity are continuously acquired from seven horses through the wearable system and a model that integrates cardiovascular and movement information to estimate the MA contribution is implemented.

Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study.

Bipolar disorder is one of the most common mood disorders characterized by large and invalidating mood swings. Several projects focus on the development of decision support systems that monitor and advise patients, as well as clinicians. Voice monitoring and speech signal analysis can be exploited to reach this goal.

A Novel Algorithm for Movement Artifact Removal in ECG Signals Acquired from Wearable Systems Applied to Horses.

This study reports on a novel method to detect and reduce the contribution of movement artifact (MA) in electrocardiogram (ECG) recordings gathered from horses in free movement conditions. We propose a model that integrates cardiovascular and movement information to estimate the MA contribution. Specifically, ECG and physical activity are continuously acquired from seven horses through a wearable system.

Speech analysis for mood state characterization in bipolar patients.

Bipolar disorders are characterized by an unpredictable behavior, resulting in depressive, hypomanic or manic episodes alternating with euthymic states. A multi-parametric approach can be followed to estimate mood states by integrating information coming from different physiological signals and from the analysis of voice. In this work we propose an algorithm to estimate speech features from running speech with the aim of characterizing the mood state in bipolar patients.