ECG-Derived Sympathetic and Parasympathetic Activity in the Healthy: an Early Lower-Body Negative Pressure Study...

Area of Science:

• Cardiovascular Physiology
• Autonomic Nervous System Research
• Biomedical Signal Processing

Background:

• Traditional heart rate variability (HRV) analysis faces challenges in accurately estimating sympathetic autonomic outflow.
• Existing methods struggle to reliably differentiate between sympathetic and parasympathetic nervous system contributions to HRV.
• A need exists for robust, non-invasive methods to assess autonomic function.

Purpose of the Study:

• To propose a novel formulation for assessing cardiac autonomic nervous system (ANS) activity.
• To introduce two distinct indices: the Sympathetic Activity Index (SAI) and the Parasympathetic Activity Index (PAI).
• To overcome limitations in current HRV analysis for estimating sympathetic and parasympathetic outflow.

Main Methods:

• Utilized RR interval series as input data.
• Combined outputs from orthonormal Laguerre filters to separate sympathetic and parasympathetic contributions to HRV spectra.
• Employed adaptive Kalman predictions for time-varying SAI and PAI estimation.
• Validated the method on data from 35 healthy subjects undergoing a lower-body negative pressure (LBNP) protocol.

Main Results:

• Demonstrated a characteristic increase in SAI and a reduction in PAI during LBNP compared to resting state.
• Confirmed the reliability of the proposed SAI and PAI measures for non-invasive autonomic assessment in healthy individuals.
• Showcased the ability to estimate autonomic function without requiring individual model calibration.

Conclusions:

• The novel SAI and PAI provide a reliable method for non-invasive autonomic nervous system assessment.
• This approach effectively disentangles sympathetic and parasympathetic activities from HRV.
• Future applications include cardiovascular assessments in various pathological states.