Abstract

Cardiac arrhythmia or irregular heartbeats are an important feature to assess the risk on sudden cardiac death and other cardiac disorders. Automatic classification of irregular heartbeats is therefore an important part of ECG analysis. We propose a tensor-based method for single- and multi-channel irregular heartbeat classification. The method tensorizes the ECG data matrix by segmenting each signal beat-by-beat and then stacking the result into a third-order tensor with dimensions channel × time × heartbeat. We use the multilinear singular value decomposition to model the obtained tensor. Next, we formulate the classification task as the computation of a Kronecker Product Equation. We apply our method on the INCART dataset, illustrating promising results.

Code description

This package provides experiment files and auxiliary files for the irregular heartbeat classification paper.

Reference

M. Boussé, G. Goovaerts, N. Vervliet, O. Debals, S. Van Huffel, L. De Lathauwer, "Irregular heartbeat classification using Kronecker product equations," in Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017, Jeju Island, South Korea), pp. 438-441, July 2017.

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