Complexity analysis of heartbeat dynamics has been widely proven effective for the characterization of cardiac autonomic control in health and disease. Due to methodological limitations, underlying physiological correlates of heartbeat complexity are not well defined yet. In this preliminary study we perform a Sample Entropy (SampEn) analysis on time series of Sympathetic and Parasympathetic Activity Indices (SAI and PAI), which allow a reliable measurement of autonomic peripheral outflow from HRV series. Irregularity of SAI and PAI time series was investigated during postural changes in 10 healthy subjects. Results show a significant SampEn increase after active stand-up with respect to resting state associated with SAI, PAI, and their ratio (SAI/PAI), suggesting that a decrease in heartbeat complexity may be sustained by increased complexity in underpinning sympathetic and vagal dynamics.

Irregularity Analysis of Sympathetic and Parasympathetic Activity Indices from HRV: A Pilot Study on Postural Changes

Citi L.;Barbieri R.;
2020-01-01

Abstract

Complexity analysis of heartbeat dynamics has been widely proven effective for the characterization of cardiac autonomic control in health and disease. Due to methodological limitations, underlying physiological correlates of heartbeat complexity are not well defined yet. In this preliminary study we perform a Sample Entropy (SampEn) analysis on time series of Sympathetic and Parasympathetic Activity Indices (SAI and PAI), which allow a reliable measurement of autonomic peripheral outflow from HRV series. Irregularity of SAI and PAI time series was investigated during postural changes in 10 healthy subjects. Results show a significant SampEn increase after active stand-up with respect to resting state associated with SAI, PAI, and their ratio (SAI/PAI), suggesting that a decrease in heartbeat complexity may be sustained by increased complexity in underpinning sympathetic and vagal dynamics.
2020
2020 11th Conference of the European Study Group on Cardiovascular Oscillations: Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1170289
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