Supervised machine learning algorithm for arrhythmia analysis

Abstract

A new machine learning algorithm for the diagnosis of cardiac arrhythmia from standard 12 lead ECG recordings is presented. The algorithm is called VFI5 for Voting Feature Intervals. VFI5 is a supervised and inductive learning algorithm for inducing classification knowledge from examples. The input to VFI5 is a training set of records. Each record contains clinical measurements, from ECG signals and some other information such as sex, age, and weight, along with the decision of an expert cardiologist. The knowledge representation is based on a recent technique called Feature Intervals, where a concept is represented by the projections of the training cases on each feature separately. Classification in VFI5 is based on a majority voting among the class predictions made by each feature separately. The comparison of the VFI5 algorithm indicates that it outperforms other standard algorithms such as Naive Bayesian and Nearest Neighbor classifiers.