Heart rate (HR) is known to be influenced by the emotional state of the subject and is commonly used as a proxy indicator of stress.The most common way humans interact is through conversation, which can be labeled as sympathetic when both participants agree on ideas, and antipathetic when they are in disagreement.
Emotional changes during conversations can alter breathing patterns, potentially impacting HRV and its connectivity metrics across different conditions. Hypothesis stands that sympathetic talks achieve larger connectivity metrics between \hrv signals of participants.
In this experiment, 38 participants were invited to engage in a conversation with a colleague while their ECG signals were synchronously recorded using a g.USBamp system, with separate grounds and reference electrodes for each participant. Subjects were randomly assigned to either a sympathetic or antipathetic conversation, based on a preliminary interview with a moderator to balance both types. Each conversation lasted approximately twenty minutes, from which both HRV signals were obtained via standard interpolation methods and R-peak detection.
Four connectivity metrics were estimated from each HRV pair: Weighted Phase Lag Index (WPLI), Phase Locking Value (PLV), Coherence (Coh), and Phase Lag Index (PLI). Statistical comparisons were conducted for each metric, showing significance in the expected direction for WPLI and Coherence (p < 0.05). Due to the limited sample size, a permutation analysis with 10,000 iterations was performed, yielding significance (p < 0.05) for Coherence only.
This experiment demonstrates that heart rate is modulated by conversational dynamics and reinforces its use as a potential index for evaluating human interactions.