Neural network classification of intracardiac ECG's

An artificial neural network has been tested for the classification of cardiac rhythms from intracardiac electrocardiograms (ECGs). It uses as inputs a small number of waveform samples and extracted parameters. The network has been found to perform better than a rate-based scheme similar to those used in commercially available implantable cardioverter-defibrillators in its ability to distinguish normal rhythms from arrhythmias. It shows, in addition, a certain ability to discriminate between a larger number of rhythms: in particular, between sinus tachycardia and slow ventricular tachycardia and between slow and fast ventricular tachycardias.<<ETX>>

[1]  J. Y. Cheung,et al.  Detection of abnormal electrocardiograms using a neural network approach , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[2]  Susan Ciarrocca Lee,et al.  Using a translation-invariant neural network to diagnose heart arrhythmia , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[3]  J M Jenkins,et al.  Intraventricular Electrogram Analysis for Ventricular Tachycardia Detection: Statistical Validation , 1990, Pacing and clinical electrophysiology : PACE.

[4]  M. Fuller,et al.  Short-time Fourier analysis of signal averaged electrocardiograms , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Emmanuel Skordalakis,et al.  Syntactic Pattern Recognition of the ECG , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Biing-Nan Hung,et al.  Spectrum analysis for ventricular tachyarrhythmias , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Michael I. Jordan.  Supervised learning and systems with excess degrees of freedom , 1988 .

[8]  E. Pietka.  Neural nets for ECG classification , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[9]  Kang-Ping Lin,et al.  Classification of QRS pattern by an associative memory model , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.