The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method.

Area of Science:

• Cardiovascular Physiology
• Autonomic Nervous System Dynamics
• Nonlinear Signal Analysis

Background:

• Congestive heart failure (CHF) is linked to autonomic dysfunction and sympathovagal imbalance, often assessed via heart rate variability (HRV).
• Understanding the directed interactions within the autonomic nervous system (ANS) is crucial for characterizing CHF pathophysiology.

Purpose of the Study:

• To investigate the directed information transfer between sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) using transfer entropy (TE).
• To evaluate the efficacy of TE in distinguishing between healthy individuals and CHF patients.

Main Methods:

• Utilized 24-h RR interval signals from healthy subjects and CHF patients from the PhysioNet database.
• Segmented signals into 5-min epochs and extracted frequency-domain features (LF, HF, LF/HF ratio).
• Calculated TE between low-frequency (LF) and high-frequency (HF) components to quantify SNS-PNS information exchange.

Main Results:

• CHF patients exhibited a significantly decreased LF/HF ratio and significantly increased TE(LF→HF) and TE(HF→LF) compared to controls.
• TE(LF→HF) consistently exceeded TE(HF→LF) in both groups, indicating directional information flow.
• A combination of LF/HF ratio, TE(LF→HF), and TE(HF→LF) achieved 83.7% accuracy in discriminating CHF.

Conclusions:

• Transfer entropy effectively quantifies directed information exchange within the ANS.
• TE serves as a valuable complement to the traditional LF/HF ratio for congestive heart failure screening.
• These findings enhance our understanding of autonomic dysfunction in CHF and offer improved diagnostic potential.