PMID: 7516545Apr 1, 1994Paper

Differentiation of beats of ventricular and sinus origin using a self-training neural network

Pacing and Clinical Electrophysiology : PACE
S J EvansM M Bodenheimer

Abstract

Despite advances in the computerized detection of arrhythmias, arrhythmia recognition by morphological waveform analysis still poses a difficult problem. Artificial neural networks, computer algorithms that are self-trained by an analog of biological synaptic modification to perform pattern recognition, hold great promise for the differentiation of various cardiac rhythms. The goal of this study was to differentiate beats of sinus and ventricular origin on a global basis and on a patient-specific basis by the use of artificial neural network analysis. Neural networks were trained to recognize digitized intracardiac electrograms (9 patients) and surface electrocardiograms (11 patients) obtained during sinus rhythm and ventricular tachycardia. After training, sinus rhythm or ventricular tachycardia beats were input into the neural network, and classified as to their origin. By the use of modified receiver operating characteristic curve plots, it was possible to differentiate with high sensitivity and specificity between beats of sinus origin and ventricular origin in all patients. The addition of high amounts of noise to the beats did not markedly degrade the performance of the surface ECG neural networks, and still allowed high ...Continue Reading

References

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Citations

Jul 14, 2004·IEEE Transactions on Bio-medical Engineering·Philip de ChazalRichard B Reilly
Dec 13, 2006·IEEE Transactions on Bio-medical Engineering·Philip de Chazal, Richard B Reilly
Sep 6, 2001·IEEE Transactions on Bio-medical Engineering·Y WangY H Xu
Feb 5, 1999·IEEE Transactions on Bio-medical Engineering·K MinamiT Toyoshima
Nov 28, 2016·Journal of Medical Systems·Juyoung ParkKyungtae Kang

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