Decision fusion of micro-variability and signal averaged ECG parameters from the QRS complex with RBF networks

Two types of measurements are usually performed from high resolution ECG recordings: (a) static parameters derived from the signal-averaged QRS complex and (b) variant markers derived from beat-to-beat recordings. It is known that an increased QRS micro-variability and ventricular late potentials are associated with an increased risk for malignant arrhythmias. However, the diagnostic power of the singular parameters is limited In this study we investigated the diagnostic ability of a decision fusion of both variant and static high-resolution ECG parameters with radial-basis-function (RBF) networks. Continuous and signal-averaged ECGs were recorded from 51 healthy volunteers without any structural heart disease and no cardiac risk factors and from 44 patients with coronary heart disease and ventricular arrhythmias. Beat-to-beat micro-variability measurement of the QRS complex and the ST-T segment was based on 250 consecutive sinus beats per individual. Signal-averaged ECGs were analyzed with the Simson method (QRSD, RMS, LAS). Two RBF networks were trained One on the three signal averaged parameters and one with the 141D variability vector The two soft decisions from each RBF network were then combined by average fusion and maximum detection into a final crisp decision which resulted in an unusually high discriminative accuracy.