| Autor: |
Karly S. Franz, Sanaz Rezaei, Tom Chau |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 183607-183615 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3512353 |
| Popis: |
Interpersonal synchrony refers to the physiological or behavioral alignment between individuals within specific social contexts. While a variety of physiological signals are used to investigate synchrony, heartrate variability (HRV) has emerged as a valuable indicator of autonomic coordination between interacting participants. Among the most popular methods for measuring HRV synchrony is linear cross-correlation. However, given the complexity of cardiac signals, nonlinear analytical approaches may be more effective in uncovering synchrony. We introduce dyadic Poincaré plot analysis (dPPA) as tool for both visualizing and quantifying interpersonal cardiac synchrony over time. The method is based on simultaneously plotting time-delayed embeddings of interbeat intervals of two interacting individuals on a single graph and deriving inter-centroid distances between the dyadic point clouds over time. We demonstrate dPPA with cardiac data from acquainted (for at least 1 year) and unacquainted dyads interacting during a 30-minute unstructured conversation. dPPA findings were compared to those of conventional cross-correlation analyses. dPPA analysis uniquely revealed that acquainted and unacquainted dyads experienced, respectively, a significant increase and decrease in cardiac synchrony over time. Furthermore, dPPA indicated that all dyads experienced heightened sympathetic nervous system activity during conversation. dPPA affords both a simple visualization and sensitive quantitative characterization of time-evolving cardiac synchrony. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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