Paper Info
Title
ECG-Derived Sympathetic and Parasympathetic Activity in the Healthy: an Early Lower-Body Negative Pressure Study Using Adaptive Kalman Prediction.
Abstract
Recent investigations have challenged the reliability of estimating sympathetic autonomic outflow from heart rate variability (HRV) analysis. Towards overcoming this long-lasting challenge, in this study we propose a new formulation for the assessment of autonomic nervous system activity on the heart based on two separate indices: the Sympathetic Activity Index (SAI) and the Parasympathetic Activity Index (PAI). Specifically, considering the RR interval series as an input, we properly combine the output of orthonormal Laguerre filters to disentangle the overlapping contribution of sympathetic and parasympathetic activities on HRV spectra. Adaptive Kalman predictions account for a time-varying SAI and PAI estimation from exemplary data gathered from 35 healthy subjects under-going a lower-body negative pressure (LBNP) protocol. Results show a defined characteristic increase (reduction) of the SAI (PAI) dynamics during LBNP with respect to the resting state condition, demonstrating the reliability of the proposed measures for a non-invasive autonomic assessment in the healthy without the need of individual model calibration. Comparison with standard HRV metrics defined in the frequency domain, as well as prospective endeavours for cardiovascular assessments in pathological states, are also discussed.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8513512
EMBC
Field
DocType
Volume
Computer vision,Autonomic nervous system,RR interval,Heart rate variability,Internal medicine,Computer science,Resting state fMRI,Cardiology,Kalman filter,Artificial intelligence
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Gaetano Valenza131448.21
luca citi216827.88
Vegard Bruun Wyller300.34
Riccardo Barbieri446070.50