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A nonlinear heartbeat dynamics model approach for personalized emotion recognition.

Gaetano Valenza, Luca Citi, Antonio Lanatà

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Area of Science:

    • Affective computing and computational neuroscience.
    • Physiological signal processing for emotion recognition.

    Background:

    • Emotion recognition often relies on autonomic nervous system (ANS) signals.
    • Traditional methods require long time-series data for reliable emotion classification.
    • Existing approaches face limitations in assessing cardiovascular dynamics during short affective stimuli.

    Purpose of the Study:

    • To develop a novel methodology for assessing cardiovascular dynamics during short-term (< 10 seconds) affective stimuli.
    • To overcome limitations of current emotion recognition approaches requiring extensive data.
    • To enable effective short-time affective assessment using personalized probabilistic frameworks.

    Main Methods:

    • Developed a personalized, fully parametric probabilistic framework based on point-process theory.
    • Modeled heartbeat events using a 2nd-order nonlinear autoregressive integrative structure.
    • Utilized instantaneous spectrum and bispectrum features from RR intervals for analysis.

    Main Results:

    • Successfully characterized emotional states of subjects exposed to standardized affective images (International Affective Picture System).
    • Achieved clear classification of two defined levels of arousal, valence, and self-reported emotional state.
    • Reached up to 90% recognition accuracy in short-time affective assessment.

    Conclusions:

    • The proposed methodology effectively assesses cardiovascular dynamics during brief affective stimuli.
    • This framework enables robust emotion recognition with short physiological recordings.
    • The findings advance the field of affective computing by enabling real-time emotion analysis.