Assessing complexity and causality in heart period variability through a model-free data-driven multivariate approach

Luca Faes, Anielle C. M. Takahashi, Luca Faes, Aparecida M. Catai, Giandomenico Nollo, Alberto Porta

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Abstract

The aim of this study is to emphasize the importance of model-free data-driven mul- tivariate approaches in describing HP variability and cardiovascular control mechanisms responsible for inducing HP changes via modifications of different cardiovascular vari- ables such as SAP and RESP. The goal was achieved through the application, a previously proposed model-free data-driven multivariate framework devised to assess complexity and causality over a multivariate set composed by several, simultaneously recorded, car- diovascular variability series (Porta et al., 2014). The approach was applied to assess the complexity of the cardiac control, through the evaluation of the amount of irregularity of HP variability in a multivariate space accounting for HP, SAP, and RESP; the degree of involvement of the cardiac baroreflex and cardiopulmonary pathway in governing cardio- vascular interactions, through the evaluation of the strength of the causal link from SAP and RESP to HP variability. Modifications of complexity and causality during supine rest- ing condition (REST) and during the orthostatic challenge resulting from active standing (STAND) were quantified as a function of age.
Lingua originaleEnglish
Titolo della pubblicazione ospiteECG Time Series Variability Analysis: Engineering and Medicine
Numero di pagine24
Stato di pubblicazionePublished - 2017

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All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Engineering(all)

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Faes, L., Takahashi, A. C. M., Faes, L., Catai, A. M., Nollo, G., & Porta, A. (2017). Assessing complexity and causality in heart period variability through a model-free data-driven multivariate approach. In ECG Time Series Variability Analysis: Engineering and Medicine