A Fourier-transform neural network algorithm achieved high sensitivity and specificity (≥0.98) in discriminating supraventricular from ventricular rhythms using human surface ECGs and intracardiac EGMs.
Does a Fourier-transform neural network accurately discriminate supraventricular from ventricular rhythms using ECGs and EGMs?
A Fourier-transform neural network algorithm demonstrates high sensitivity and specificity (≥0.98) for real-time discrimination of ventricular from supraventricular arrhythmias.
Effect estimate: Sensitivity and specificity ≥0.98
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.
Minami et al. (1999) studied Ventricular tachyarrhythmia. Fourier-transform neural network was evaluated on Discrimination of supraventricular rhythms from ventricular ones (Sensitivity and specificity ≥0.98). A Fourier-transform neural network algorithm achieved high sensitivity and specificity (≥0.98) in discriminating supraventricular from ventricular rhythms using human surface ECGs and intracardiac EGMs.