Using a Translation-Invariant Neural Network to Diagnose Heart Arrhythmia

Abstract

Distinctive electrocardiogram (EeG) patterns are created when the heart is beating normally and when a dangerous arrhythmia is present. Some devices which monitor the EeG and react to arrhythmias parameterize the ECG signal and make a diagnosis based on the parameters. The author discusses the use of a neural network to classify the EeG signals directly. without parameterization. The input to such a network must be translation-invariant. since the distinctive features of the EeG may appear anywhere in an arbritrarily-chosen EeG segment. The input must also be insensitive to the episode-to-episode and patient-to-patient variability in the rhythm pattern.