title: Arousal recognition system based on heartbeat dynamics during auditory elicitation.
abstract: This study reports on the recognition of different arousal levels, elicited by affective sounds, performed using estimates of autonomic nervous system dynamics. Specifically, as a part of the circumplex model of affect, arousal levels were recognized by properly combining information gathered from standard and nonlinear analysis of heartbeat dynamics, which was derived from the electrocardiogram (ECG). Affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal. A group of 27 healthy volunteers underwent such elicitation while ECG signals were continuously recorded. Results showed that a quadratic discriminant classifier, as applied implementing a leave-one-subject-out procedure, achieved a recognition accuracy of 84.26%. Moreover, this study confirms the crucial role of heartbeat nonlinear dynamics for emotion recognition, hereby estimated through lagged Poincare plots.
authors.forename: Mimma
authors.initials: M
authors.lastname: Nardelli
authors.forename: Gaetano
authors.initials: G
authors.lastname: Valenza
authors.forename: Alberto
authors.initials: A
authors.lastname: Greco
authors.forename: Antonio
authors.initials: A
authors.lastname: Lanata
authors.forename: Enzo Pasquale
authors.initials: EP
authors.lastname: Scilingo
mesh_terms: D000328:Adult; D000465:Algorithms; D001143:Arousal / Q000502:physiology*; D001341:Autonomic Nervous System / Q000502:physiology; D004562:Electrocardiography; D004644:Emotions / Q000502:physiology; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D017711:Nonlinear Dynamics