title: Differentiation of beats of ventricular and sinus origin using a self-training neural network.
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 sensitivity in differentiating beats of sinus origin from beats of ventricular origin, especially when noise was added to the training set. Neural networks provided sensitive and specific detection of cardiac electrical activity during sinus rhythm and ventricular tachycardia, and may play an important role in allowing development of improved arrhythmia recognition and management systems.
authors.affiliation: Harris Chasanoff Heart Institute, Department of Medicine, Long Island Jewish Medical Center, New Hyde Park, NY 11042.
authors.forename: S J
authors.initials: SJ
authors.lastname: Evans
authors.forename: H
authors.initials: H
authors.lastname: Hastings
authors.forename: M M
authors.initials: MM
authors.lastname: Bodenheimer
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D003937:Diagnosis, Differential; D004562:Electrocardiography; D004594:Electrophysiology; D005246:Feedback; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012849:Sinoatrial Node / Q000502:physiology*; D012984:Software; D017180:Tachycardia, Ventricular / Q000503:physiopathology*