A novel Heart Rate Variability analysis using Lagged Poincaré plot: A study on hedonic visual elicitation

Area of Science:

• Cardiology
• Psychophysiology
• Biomedical Engineering

Background:

• Heart Rate Variability (HRV) analysis is crucial for understanding autonomic nervous system function.
• Traditional HRV methods may not fully capture complex emotional responses.
• The International Affective Picture System (IAPS) provides standardized emotional stimuli (arousal and valence).

Purpose of the Study:

• To propose and validate a novel hybrid method, LPP_symb, for emotion recognition using HRV.
• To investigate the autonomic response to varying levels of pleasant and unpleasant emotional stimuli.
• To assess the efficacy of LPP_symb in classifying emotional states.

Main Methods:

• Development of LPP_symb, a hybrid method combining Lagged Poincaré Plot (LPP) quantifiers and symbolic dynamics.
• Application of LPP_symb to HRV data collected during exposure to IAPS pictures with controlled arousal and valence.
• Implementation of a pattern recognition system using Leave-One-Subject-Out (LOSO) cross-validation and a Quadratic Discriminant Classifier (QDC).

Main Results:

• The LPP_symb method was applied to HRV data from 22 healthy subjects across four experimental sessions.
• The pattern recognition system achieved a classification accuracy of 71.59% in distinguishing pleasant from unpleasant emotional states.
• Significant differences in HRV patterns were observed in response to varying emotional valence and arousal levels.

Conclusions:

• The proposed LPP_symb method shows promise as a tool for objective emotion recognition.
• HRV analysis using LPP_symb can effectively differentiate between autonomic responses to pleasant and unpleasant stimuli.
• This approach offers a novel avenue for non-invasive emotion detection in various applications.