Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network

We have developed a method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat. Changes in QRS complexes due to rhythm origination and conduction path were observed with the Fourier transform, and three kinds of rhythms were discriminated by a neural network. In this paper, the potential of our method for clinical uses and real-time detection was examined using human surface ECG's and intracardiac electrograms (EGM's). The method achieved high sensitivity and specificity (> or = 0.98) in discrimination of supraventricular rhythms from ventricular ones. We also present a hardware implementation of the algorithm on a commercial single-chip CPU.