Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics.
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.
1] Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA [2] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA [3] Department of Information Engineering and Research Centre E Piaggio, University of Pisa, Pisa, Italy.
Gaetano
G
Valenza
1] Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA [2] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA [3] School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO43SQ, UK.
Luca
L
Citi
Department of Information Engineering and Research Centre E Piaggio, University of Pisa, Pisa, Italy.
Antonio
A
Lanatá
Department of Information Engineering and Research Centre E Piaggio, University of Pisa, Pisa, Italy.
Enzo Pasquale
EP
Scilingo
1] Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA [2] Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Riccardo
R
Barbieri
D000465:Algorithms; D003198:Computer Simulation; D004562:Electrocardiography; D004644:Emotions / Q000502:physiology*; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008955:Models, Cardiovascular; D017711:Nonlinear Dynamics; D046709:Pattern Recognition, Physiological; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine