Valenza, GaetanoCiti, LucaLanatá, AntonioScilingo, Enzo PasqualeBarbieri, Riccardo2014-07-072014Valenza, Gaetano, Luca Citi, Antonio Lanatá, Enzo Pasquale Scilingo, and Riccardo Barbieri. 2014. “Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics.” Scientific Reports 4 (1): 4998. doi:10.1038/srep04998. http://dx.doi.org/10.1038/srep04998.2045-2322http://nrs.harvard.edu/urn-3:HUL.InstRepos:12406974Emotion 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.en-USJournal Article2014-07-0710.1038/srep04998