Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics

Abstract

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.

Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects

“…The use of inhomogeneous point-process on heartbeat dynamics allows to obtain instantaneous time domain and spectral estimates, which can be considered as covariate measures of brain-heart interaction during emotional processing. Details on the inhomogeneous point-process modelling can be found in [68][69][70]. Briefly, we model the probability function of the next heartbeat given the past R-events.…”

Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardio-Respiratory Nonstationary Dynamics

“…Moreover, our use of linear parametric models to predict non-Gaussian (i.e., Inverse-Gaussian) statistics should capture some of the cardiovascular system nonlinearity. This is different from the approach proposed in most of our previous studies (e.g., [8], [9], [17], [60]- [62]) which dealt with monovariate nonlinear, non-stationary physiological systems.…”

Instantaneous monitoring of heart beat dynamics during anesthesia and sedation

“…Most importantly, we will be able to consider a novel measure of RSA, which accounts for respiratory pattern variations in assessing sympathovagal dynamics. Our future studies will determine by which degree a combined index of the ANS measures is able to accurately quantify sedation in controlled scenarios, also estimating recently proposed instantaneous nonlinear measures based on high-order spectral analysis and entropy [70,[83][84][85][86][87][88][89][90]. We will devise classifiers which might provide enough power to produce a combination of measures of autonomic outflow validating the assessment for each single subject, thus paving the way for the feasibility for a realtime monitoring tool able to track sedation in uncontrolled scenarios.…”