The application of methods of non-linear dynamics for the improved and predictive recognition of patients threatened by sudden cardiac death

• Published in: Cardiovascular research 1996-03, Vol.31 (3), p.419-433
• Main Authors: VOSS, A, KURTHS, J, KLEINER, H. J, WITT, A, WESSEL, N, SAPARIN, P, OSTERZIEL, K. J, SCHURATH, R, DIETZ, R
• Format: Article
• Language: English
• Subjects: Biological and medical sciences; Cardiac dysrhythmias; Cardiology. Vascular system; Death, Sudden, Cardiac - prevention & control; Electrocardiography; Heart; Heart Rate - physiology; Humans; Medical sciences; Myocardial Infarction - physiopathology; Nonlinear Dynamics; Predictive Value of Tests; Risk; Signal Processing, Computer-Assisted
• ISSN: 0008-6363; 1755-3245
• DOI: 10.1016/0008-6363(96)00008-9

This study introduces new methods of non-linear dynamics (NLD) and compares these with traditional methods of heart rate variability (HRV) and high resolution ECG (HRECG) analysis in order to improve the reliability of high risk stratification. Simultaneous 30 min high resolution ECG's and long-term ECG's were recorded from 26 cardiac patients after myocardial infarction (MI). They were divided into two groups depending upon the electrical risk, a low risk group (group 2, n = 10) and a high risk group (group 3, n = 16). The control group consisted of 35 healthy persons (group 1). From these electrocardiograms 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. Applying discriminant function techniques on HRV analysis the parameters of non-linear dynamics led to an acceptable differentiation between healthy persons and high risk patients of 96%. The time domain and frequency domain parameters were successful in less than 90%. The combination of parameters from all domains and a stepwise discriminant function separated these groups completely (100%). Use of this discriminant function classified three patients with apparently low (no) risk into the same cluster as high risk patients. The combination of the HRECG and HRV analysis showed the same individual clustering but increased the positive value of separation. The methods of NLD describe complex rhythm fluctuations and separate structures of non-linear behavior in the heart rate time series more successfully than classical methods of time and frequency domains. This leads to an improved discrimination between a normal (healthy persons) and an abnormal (high risk patients) type of heart beat generation. Some patients with an unknown risk exhibit similar patterns to high risk patients and this suggests a hidden high risk. The methods of symbolic dynamics and renormalized entropy were particularly useful measures for classifying the dynamics of HRV.