Irregular heartbeats detection using tensors and Support Vector Machines

Abstract

The automatic analysis of Heart Rate Variability in records of ambulatory electrocardiogram (AECG) requires the detection of irregular heartbeats which cannot be included in the ansalysis. This article presents a novel approach for detecting irregular beats using tensors and Support Vector Machines. After signal filtering, for each record of the database a third order tensor was constructed. Next, a rank-3 Canonical Polyadic Decomposition (CPD) was applied. CPD yields three loading matrices corresponding to the modes space (channel), time course and heartbeats respectively. The heartbeat mode matrix was used as the input of a linear Support Vector Machine (SVM) classifier. The SVM was trained for classifying between irregular and normal heartbeats. The training set was randomly selected from the 2% of the patterns in each record. The classifiers show a global accuracy of 97.2%. The results suggest that this approach is a promising method for detecting irregular heartbeats.