ECG pattern analysis using empirical mode decomposition demonstrated the feasibility of determining emotional valence from ECG morphology, achieving up to 89% accuracy in 44 subjects.
Emotion modeling and recognition has drawn extensive attention from disciplines such as psychology, cognitive science, and, lately, engineering. Although a significant amount of research has been done on behavioral modalities, less explored characteristics include the physiological signals. This work brings to the table the ECG signal and presents a thorough analysis of its psychological properties. The fact that this signal has been established as a biometric characteristic calls for subject-dependent emotion recognizers that capture the instantaneous variability of the signal from its homeostatic baseline. A solution based on the empirical mode decomposition is proposed for the detection of dynamically evolving emotion patterns on ECG. Classification features are based on the instantaneous frequency (Hilbert-Huang transform) and the local oscillation within every mode. Two experimental setups are presented for the elicitation of active arousal and passive arousal/valence. The results support the expectations for subject specificity, as well as demonstrating the feasibility of determining valence out of the ECG morphology (up to 89 percent for 44 subjects). In addition, this work differentiates for the first time between active and passive arousal, and advocates that there are higher chances of ECG reactivity to emotion when the induction method is active for the subject.
Agrafioti et al. (Wed,) conducted a other in Emotion detection (n=44). ECG pattern analysis using empirical mode decomposition was evaluated on Determining emotional valence from ECG morphology. ECG pattern analysis using empirical mode decomposition demonstrated the feasibility of determining emotional valence from ECG morphology, achieving up to 89% accuracy in 44 subjects.