Point process time-frequency analysis of dynamic respiratory patterns during meditation practice.
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....
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 3; pp. 261 - 276 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104534772&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104534772 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2012 vid: 50 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104534772 NLM22350435 2011490739 10.1007/s11517-012-0866-z NLM22350435 PMC3341131 104534772 ppf: 261 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Point process time-frequency analysis of dynamic respiratory patterns during meditation practice. aug: au: Kodituwakku S Lazar SW Indic P Chen Z Brown EN Barbieri R Kodituwakku, Sandun Lazar, Sara W Indic, Premananda Chen, Zhe Brown, Emery N Barbieri, Riccardo affil: Applied Signal Processing Group, School of Engineering, The Australian National University, Canberra, Australia sug: subj: Arrhythmia, Sinus Physiopathology Meditation Models, Biological Respiratory Mechanics Physiology Adult Algorithms Autonomic Nervous System Physiology Female Male Middle Age Signal Processing, Computer Assisted Adult: 19-44 years Middle Aged: 45-64 years Female Male ab: 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 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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