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| Content Provider | PubMed Central |
|---|---|
| Author | Valenza, Gaetano Citi, Luca Antonio, Lanatá Scilingo, Enzo Pasquale Barbieri, Riccardo |
| Copyright Year | 2014 |
| 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. |
| Related Links | http://dx.doi.org/10.1038/srep04998 |
| Starting Page | 4998 |
| File Format | |
| ISSN | 20452322 |
| e-ISSN | 20452322 |
| Journal | Scientific Reports |
| Volume Number | 4 |
| Language | English |
| Publisher | Nature Publishing Group |
| Publisher Date | 2014-05-01 |
| Access Restriction | Open |
| Rights Holder | Nature Publishing Group |
| Subject Keyword | Science and technology Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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