Using Recurrence Quantification Analysis to Quantify the Physiological Synchrony in Dyadic ECG Data

Socio-physiological compliance (SPC) is highly regarded in team settings and has attracted significant interest from science of teams researchers. Linear measures of heart rate variability (HRV) has been widely adopted to observe and analyse SPC by evaluating physiological interconnection between members of collaborating teams. In the recent past, nonlinear measurements such as Recurrent Quantification Analysis (RQA) has gained a significant momentum in the measurement of physical stress experienced by individuals using the electrocardiogram (ECG) recordings. In a team context, however, literature shows variants of RQA such as Cross-RQA and Multivariate-RQA have been commonly employed to analyse behavioural data from accelerometer or eye-tracking recordings, respectively. The current study presents an analysis employing RQA in a team setting using the HRV data derived from ECG recordings. From individuals, RQA is employed to extract nonlinear features of HRV, and the physiological synchrony between cognitively engaging participants is quantified via Pearson’s correlation coefficient. Most importantly, the study explores the effectiveness of these nonlinear HRV features as alternate options to assess SPC. In terms of capabilities to distinguish collaborating pairs’, the RQA measures; recurrence rate, laminarity, determinism and entropy, were observed to be robust in short-term window lengths and equally sensitive compared to linear features of HRV.