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.
Ventricular tachyarrhythmia
Fourier-transform neural network
Discrimination of supraventricular rhythms from ventricular ones — Sensitivity and specificity ≥0.98
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.
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Kentaro Minami
Dokkyo University
Hiroyuki Nakajima
Akita University
Takeshi Toyoshima
Tokyo Medical and Dental University
IEEE Transactions on Biomedical Engineering
Teikyo University
Medtronic (Japan)
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Minami et al. (Fri,) conducted a other in 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.
synapsesocial.com/papers/6a12bb2683732aa7db9e315a — DOI: https://doi.org/10.1109/10.740880