Point process time-frequency analysis of respiratory sinus arrhythmia under altered respiration dynamics
Author(s)Kodituwakku, Sandun; Lazar, Sara W.; Indic, Premananda; Brown, Emery N.; Barbieri, Riccardo
MetadataShow full item record
Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heartbeat. We propose an algorithm for quantifying instantaneous RSA as applied to heart beat interval and respiratory recordings under dynamic respiration conditions. The blood volume pressure derived heart beat series (pulse intervals, PI) are modeled as an inverse gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PI and respiration values observed at the beats. A point process maximum likelihood algorithm is used to estimate the model parameters, and instantaneous RSA is estimated by a frequency domain transfer function approach. The model is statistically validated using Kolmogorov-Smirnov (KS) goodness-of-fit analysis, as well as independence tests. The algorithm is applied to subjects engaged in meditative practice, with distinctive dynamics in the respiration patterns elicited as a result. Experimental results confirm the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states.
DepartmentHarvard University--MIT Division of Health Sciences and Technology; Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. EMBC 2009
Institute of Electrical and Electronics Engineers
Kodituwakku, S et al. “Point Process Time-frequency Analysis of Respiratory Sinus Arrhythmia Under Altered Respiration Dynamics.” IEEE, 2010. 1622–1625. © Copyright 2010 IEEE.
Final published version
INSPEC Accession Number: 11650368