Lagged Transfer Entropy Analysis to Investigate Cardiorespiratory Regulation in Newborns during Sleep

The autonomic nervous system (ANS) acts modulating the cardiac and respiratory systems by means of the sympathetic and parasympathetic branches. In this work, we propose to employ Transfer Entropy (TE) with the aim of disambiguating the contributions of the two branches over cardiorespiratory regulation in newborns during sleep. Specifically, we computed TE on the original time series representative of the two subsystems, namely Heart Rate Variability (HRV) and Respiration (RESP). Furthermore, we employed a lagged version of the two original signals to derive a TE estimation capable of providing and insight on the short-term memory between the two systems. Results show the information transfer quantified by TERESP→RR decaying rapidly as the shift between the two time series increases. On the other hand, TERR→RESP exhibits a slower but prolonged interaction, which lasts over numerous lags. The novel approach presented in this work affords the potential to assess infants’ ANS developmen t in terms of the quantification of cardiorespiratory control functioning.