Binary symbolic dynamics classifies heart rate variability patterns linked to autonomic modulations.

Area of Science:

• Physiology
• Biomedical Engineering
• Cardiology

Background:

• Heart rate variability (HRV) analysis provides insights into cardiac autonomic control.
• Symbolic dynamics of HRV can detect short-term autonomic changes, but specific pattern-activity links require elucidation.

Purpose of the Study:

• To investigate the relationship between symbolic patterns in heart rate data and autonomic nervous system activity.
• To determine if symbolic dynamics can differentiate sympathetic and parasympathetic modulations during physiological stress.

Main Methods:

• Analysis of binary symbolic dynamics of instantaneous heart rate acceleration/deceleration.
• Quantification of pattern regularity using Approximate Entropy (ApEn) on 8-bit sequences.
• Correlation analysis of pattern occurrence with increasing head-up tilt angles (0-90°) in 17 healthy subjects.

Main Results:

• Regular binary patterns increased with tilt angle, indicating heightened sympathetic activity.
• Irregular binary patterns decreased with tilt angle, suggesting reduced parasympathetic activity.
• Symbolic dynamics parameters correlated with tilt angle and reflected autonomic changes distinct from spectral HRV markers.

Conclusions:

• Binary symbolic dynamics effectively capture autonomic modulations during head-up tilt.
• Regular patterns are associated with sympathetic activity, and irregular patterns with parasympathetic activity.
• Symbolic dynamics offer complementary information to spectral analysis for HRV assessment.