Real-time ventricular arrhythmia detection with Fourier analysis and neural network

We developed a system which detects life threatening ventricular arrhythmias with respect to each beat. In this paper, we applied the system to human intracardiac electrogram (EGM) data for demonstrating the clinical potential. The system analyzes Fourier spectrum of the EGM signal corresponding to an individual QRS complex, and classifies it to three kinds of rhythm origins using a neural network. In our study, supra-ventricular rhythms were classified from ventricle originated rhythms with high sensitivities and specificities.