THE APPLICATION OF METHODS OF NONLINEAR DYNAMICS FOR THE IMPROVED ANDPREDICTIVE RECOGNITION OF PATIENTS THREATENED BY SUDDEN CARDIAC DEATH

Objectives: This study introduces new methods of non-linear dynamics ( NLD) and compares these with traditional methods of heart rate variabi lity (HRV) and high resolution ECG (HRECG) analysis in order to improv e the reliability of high risk stratification. Methods: Simultaneous 3 0 min high resolution ECG's and long-term ECG's were recorded from 26 cardiac patients after myocardial infarction (MI). They were divided i nto two groups depending upon the electrical risk, a low risk group (g roup 2, n = 10) and a high risk group (group 3, n = 16). The control g roup consisted of 35 healthy persons (group 1). From these electrocard iograms we extracted standard measures in time and frequency domain as well as measures from the new non-linear methods of symbolic dynamics and renormalized entropy, Results: Applying discriminant function tec hniques on HRV analysis the parameters of non-linear dynamics led to a n acceptable differentiation between healthy persons and high risk pat ients of 96%. The time domain and frequency domain parameters were suc cessful in less than 90%. The combination of parameters from all domai ns and a stepwise discriminant function separated these groups complet ely (100%). Use of this discriminant function classified three patient s with apparently low (no) risk into the same cluster as high risk pat ients. The combination of the HRECG and HRV analysis showed the same i ndividual clustering but increased the positive value of separation. C onclusions: The methods of NLD describe complex rhythm fluctuations an d separate structures of non-linear behavior in the heart rate time se ries more successfully than classical methods of time and frequency do mains. This leads to an improved discrimination between a normal (heal thy persons) and an abnormal (high risk patients) type of heart beat g eneration. Some patients with an unknown risk exhibit similar patterns to high risk patients and this suggests a hidden high risk. The metho ds of symbolic dynamics and renormalized entropy were particularly use ful measures for classifying the dynamics of HRV.