Detection of irregular heartbeats using tensors

Automatic classification of heartbeats in different categories is important for ECG analysis. The number of irregular heartbeats in a signal can for example be used as a risk stratifier for sudden cardiac death. Current heart-beat classification methods typically use time or frequency domain features to characterize heartbeats. We propose the use of tensors to incorporate the structural information that is present in multilead ECG channels. Since different ECG leads provide information on a particular orientation in space, more robust detection can be done if all leads are considered. After preprocessing and heartbeat detection using wavelet-based methods, the ECG signal is segmented beat-by-beat. The different heartbeats are then placed in a three-dimensional tensor with dimensions time, channels and heartbeats. Canonical Polyadic Decomposition is used to decompose the tensor. The results are three loading vectors, corresponding to the dimensions of the original tensor. Through analysis of these loading vectors, irregular heartbeats can be detected using a simple thresholding procedure. The method has been applied to a subset of the St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database available on Physionet. When applying the method to the first 10 signals, we obtain a mean sensitivity and specificity of more than 90%. These results indicate that the presented method is a new and reliable way of performing irregular heartbeat detection.