Improved analysis of heart rate variability by methods of nonlinear dynamics

Abstract

The traditional analysis of heart rate variability (HRV) in the time and frequency domains seems to be an independent predictive marker for sudden cardiac death. Because the usual applied methods of HRV analysis describe only linear or strong periodic phenomena, the authors have developed new methods of HRV analysis based on nonlinear dynamics. In that way, parameters are extracted that quantify more complex processes and their complicated relationships. These methods are symbolic dynamics that describes the beat-to-beat dynamics and renormalized entropy that compares the complexity of power spectra on a normalized energy level. In an initial investigation, the HRV of 35 healthy subjects and 39 cardiac patients have been analyzed. Using discriminant functions, the authors found an optimal (100%) differentiation between the group of healthy subjects (even using only an age-matched subgroup of 12 subjects) and that of patients after myocardial infarction with a high electrical risk (Lown 4b). Applying this discriminant function to a group of patients with low electrical risk, four patients show the same behavior indicative of a high risk score, which might be a sign for a hidden high risk, two patients show healthy behavior, and the remaining patients show a separate pattern. The use of new methods of nonlinear dynamics in combination with parameters of the time and frequency domains in HRV offers possibilities for improved classification of HRV behavior. It is suggested that this could lead to a more detailed classification of individual high risk.