Point process time–frequency analysis of dynamic respiratory patterns during meditation practice

Abstract

Respiratory sinus arrhythmia (RSA) is largely mediated by the autonomic nervous system through its modulating influence on the heart beats. We propose a robust algorithm for quantifying instantaneous RSA as applied to heart beat intervals and respiratory recordings under dynamic breathing patterns. The blood volume pressure derived heart beat series (pulse intervals, PIs) are modeled as an inverse gaussian point process, with the instantaneous mean PI modeled as a bivariate regression incorporating both past PIs 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 via a frequency domain transfer function evaluated at instantaneous respiratory frequency where high coherence between respiration and PIs is observed. 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. The presented analysis confirms the ability of the algorithm to track important changes in cardiorespiratory interactions elicited during meditation, otherwise not evidenced in control resting states, reporting statistically significant increase in RSA gain as measured by our paradigm.

Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate Variability

“…In the future research, we are planning to develop a similar study on a larger (although nonpublic) dataset of ECG recordings digitalized at the same sampling frequency and annotated with a stated procedure, i.e., the database of Regione Campania Network [43]. Moreover, nonlinear [44], [45] and/or point process time-frequency [46] analysis could provide additional useful measures for automatic classification. Finally, the classification algorithms could be integrated in portable sensing devices [47]- [49].…”

Instantaneous monitoring of heart beat dynamics during anesthesia and sedation

“…We use an extension of the statistical point process model that we recently developed [68] to assess instantaneous estimates of respiratory sinus arrhythmia (RSA) from both ECG and respiration. Where respiratory information is available, we compute two RSA measures: the RSA gain at maximum coherence and RSA gain at maximum frequency.…”

“…The instantaneous RSA assessments provide information that complements the standard HRV measures, particularly in the high frequency (HF) range, as well as indices such as the analgesia nociception index (ANI) and the surgical stress index (SSI) [53,56,57]. Importantly, only our new measures of RSA make it possible to reliably evaluate RSA in waning breathing conditions or whenever subjects show slow or unusual respiratory patterns [68].…”

“…In previous studies, we established a paradigm for using point process methods for analyzing heartbeat dynamics [64,65]. We have also assessed sympathovagal indices of heart rate variability, as well as baroreflex and respiratory sinus arrhythmia [66][67][68][69][70], and we have successfully applied these methods to protocols including induction and emergence from anesthesia [71][72][73][74][75]. We have further identified specific measures of complexity and nonlinearity associated with loss of consciousness during anesthesia [75].…”

The beneficial effects of meditation: contribution of the anterior cingulate and locus coeruleus

“…Exercise spontaneously activates the LC (Haxhiu et al, 2003) and dilates the airways (Warren et al, 1984; Haxhiu et al, 2003). The dilation of the airways by the LC via the NA may contribute to the decreased respiratory rate and HF-HRV, which are associated with meditation (Lazar et al, 2005; Tang et al, 2009; Kodituwakku et al, 2012) and yoga. The AC appears to be a critical component in the circuitry that links the development of peripheral symptoms with emotion and cognition in asthma (Rosenkranz and Davidson, 2009).…”