We compare different estimators of a popular en-tropy-based nonlinear dynamic measure, i.e. the conditional entropy (CE), as regards their ability to assess the complexity and nonlinearity of short-term heart rate variability (HRV). The CE is computed using binning, kernel and nearest neighbor entropy estimators in HRV time series measured from young, old and post-myocardial infarction patients studied at rest and during orthostatic stress. We find that the three estimators yield similar patterns of CE, but different patterns of nonlinear dynamics, across groups and conditions. These results suggest that the strategy for CE estimation is not crucial for the quantification of complexity, but has a remarkable impact on the detection of nonlinear HRV dynamics.

Faes, L., Pernice, R., Nollo, G. (2020). Entropy-Based Detection of Complexity and Nonlinearity in Short-Term Heart Period Variability under different Physiopathological States. In 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO) (pp. 1-2) [10.1109/ESGCO49734.2020.9158151].

Entropy-Based Detection of Complexity and Nonlinearity in Short-Term Heart Period Variability under different Physiopathological States

Faes, Luca
;
Pernice, Riccardo;
2020-01-01

Abstract

We compare different estimators of a popular en-tropy-based nonlinear dynamic measure, i.e. the conditional entropy (CE), as regards their ability to assess the complexity and nonlinearity of short-term heart rate variability (HRV). The CE is computed using binning, kernel and nearest neighbor entropy estimators in HRV time series measured from young, old and post-myocardial infarction patients studied at rest and during orthostatic stress. We find that the three estimators yield similar patterns of CE, but different patterns of nonlinear dynamics, across groups and conditions. These results suggest that the strategy for CE estimation is not crucial for the quantification of complexity, but has a remarkable impact on the detection of nonlinear HRV dynamics.
2020
Settore ING-INF/06 - Bioingegneria Elettronica E Informatica
978-1-7281-5751-1
Faes, L., Pernice, R., Nollo, G. (2020). Entropy-Based Detection of Complexity and Nonlinearity in Short-Term Heart Period Variability under different Physiopathological States. In 2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO) (pp. 1-2) [10.1109/ESGCO49734.2020.9158151].
File in questo prodotto:
File Dimensione Formato  
2020_Faes_ESGCO2020_published.pdf

Solo gestori archvio

Descrizione: Articolo pubblicato
Tipologia: Versione Editoriale
Dimensione 2.52 MB
Formato Adobe PDF
2.52 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
2020_Faes_ESGCO2020_postprint.pdf

Solo gestori archvio

Descrizione: Post-print
Tipologia: Post-print
Dimensione 560.61 kB
Formato Adobe PDF
560.61 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/430398
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact