A Neural Network System for Detection of Atrial Fibrillation in Ambulatory Electrocardiograms

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Abstract
A neural network classifier has been designed, which is able to distinguish atrial fibrillation (AF) from other supraventricular arrhythmias in ambulatory (Holter) ECGs. The classification algorithm uses a rhythm analysis that considers the ECG to be a time series of RR interval durations. This is combined with an analysis of baseline morphology that considers the morphological characteristics of the non-QRS portions of the waveform. A backpropagation-based neural network has been used as part of the classifier implementation. When applied to a library consisting exclusively of 42,970 examples of AF and other supraventricular rhythm disturbances validated by an experienced cardiologist, the algorithm demonstrated a sensitivity of 82.4% for 10-beat runs of paroxysmal atrial fibrillation (PAF) and a specificity of 96.6%. Since this system has been implemented as a postprocessor to a conventional automated Holter system, operating only on segments of ECG that are known to contain supraventricular arrhythmias rather than ventricular arrhythmias or sinus rhythm, it can be added to most existing Holter processing systems without significantly increasing the average time to process a tape. A neural network system has been designed, which can potentially provide, for the first time, an accurate, quantitative technique to determine the natural history of PAF and to evaluate potential treatments for PAF.