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  • 标题:Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics
  • 本地全文:下载
  • 作者:Gaetano Valenza ; Luca Citi ; Antonio Lanatá
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2015
  • 卷号:4
  • 期号:1
  • DOI:10.1038/srep04998
  • 语种:English
  • 出版社:Springer Nature
  • 摘要: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.
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