State-Transition Dynamics in the HR--Deceleration Capacity Feature Space: Passive Assessment of Autonomic Dysfunction from 24-Hour Heart Rate

Autonomic dysfunction is an important predictor of mortality in elderly people, patients with Type 2 Diabetes, and heart failure. Standard clinical tests such as the Valsalva maneuver are burdensome to perform. Researchers developed a new method called HR-DC State-Transition Dynamics that analyzes 24-hour heart rate recordings and maps them into four physiological states. This passive method does not require active patient testing. In a pilot study with 10 participants, it showed high measurement reliability (ICC >0.85) across different days. In a study with 209 participants divided into six groups, the algorithm distinguished healthy elderly subjects from pathological patients with accuracy AUC = 0.85. The metrics also correlated with age (R2=0.33). In a clinical subset of 26 patients, relaxation metrics predicted Valsalva Ratio (R2=0.58) and excitation metrics predicted blood pressure drop during tilt test (R2=0.62).