Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network
- 1 January 1999
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Biomedical Engineering
- Vol. 46 (2) , 179-185
- https://doi.org/10.1109/10.740880
Abstract
The authors 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 the authors' method for clinical uses and real-time detection was examined using human surface ECGs and intracardiac electrograms (EGMs). The method achieved high sensitivity and specificity (>0.98) in discrimination of supraventricular rhythms from ventricular ones. The authors also present a hardware implementation of the algorithm on a commercial single-chip CPU.Keywords
This publication has 12 references indexed in Scilit:
- Arrhythmia diagnosis system which can distinguish atrial rhythms from ventricular rhythmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Arrhythmia diagnosis with discrimination of rhythm origin and measurement of heart-rate variationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Implantable cardioverter defibrillatorsProceedings of the IEEE, 1996
- External defibrillators and emergency external pacemakersProceedings of the IEEE, 1996
- Detection algorithms in implantable cardioverter defibrillatorsProceedings of the IEEE, 1996
- Detecting ventricular fibrillationIEEE Engineering in Medicine and Biology Magazine, 1995
- Comparing wavelet transforms for recognizing cardiac patternsIEEE Engineering in Medicine and Biology Magazine, 1995
- Differentiation of Beats of Ventricular and Sinus Origin Using a Self‐Training Neural NetworkPacing and Clinical Electrophysiology, 1994
- Recognition of ventricular fibrillation using neural networksMedical & Biological Engineering & Computing, 1994
- Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic systemJournal of Biomedical Engineering, 1989