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

Abstract

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.

Heart rate variability in bipolar disorder and borderline personality disorder: a clinical review

“…Although these non-linear methods of quantifying HRV may give more insight into the complex interactions linked to the cardiovascular system, it is much more difficult to give clinical interpretation to their metrics. 8 12 13 …”

Multiscale entropy analysis of biological signals: a fundamental bi-scaling law

“…To better cope with such complex data, recently, Costa et al ( 2005 ) have introduced an interesting method, the multiscale entropy (MSE) analysis. MSE has found numerous applications in various types of biosignal analysis, including fetal heart rate monitoring (Cao et al, 2006 ), assessment of EEG dynamical complexity in Alzheimer's disease (Mizuno et al, 2010 ), classification of surface EMG of neuromuscular disorders (Istenic et al, 2010 ), heart rate analysis for predicting hospital mortality (Norris et al, 2008 ), and analysis of hear beat interval and blood flow for characterizing psychological dimensions in non-pathological subjects (Nardelli et al, 2015 ). MSE has also been extended to multivariate MSE (Ahmed and Mandic, 2011 ) and multiscale permutation entropy (Li et al, 2010 ).…”

A Wearable System for the Evaluation of the Human-Horse Interaction: A Preliminary Study

“…Hence, an increased number of measures, including further variables, is needed to clarify the phenomena involved in the human-horse interaction. In conclusion, this work poses the basis for the application of novel high level signal processing techniques for stationary and non-stationary signals [113,114], already used in investigating human-to-human interaction [115][116][117], to animals, and particularly to horses, in order to objectively reveal interesting responses of both the central and autonomic nervous system (ANS) for a particular uncommon stimulation. Moreover, the achieved results lead us to conclude that a quantitative measure of the human-horse interaction is viable, and it could be very effective in many fields of application, for example in therapy assisted by equine (EAT) [2,118] or for controlling the effect of therapeutic horseback riding [119].…”