Irregular heartbeats detection using tensors and Support Vector Machines

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.

[1]  Muammer Gökbulut,et al.  Performance comparison of wavelet thresholding techniques on weak ECG signal denoising , 2013 .

[2]  U. Rajendra Acharya,et al.  Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework , 2013, Knowl. Based Syst..

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  Nadine Eberhardt,et al.  Bioelectrical Signal Processing In Cardiac And Neurological Applications , 2016 .

[5]  Conor Heneghan,et al.  Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea , 2003, IEEE Transactions on Biomedical Engineering.

[6]  Yu-Liang Hsu,et al.  ECG arrhythmia classification using a probabilistic neural network with a feature reduction method , 2013, Neurocomputing.

[7]  David Menotti,et al.  ECG arrhythmia classification based on optimum-path forest , 2013, Expert Syst. Appl..

[8]  Marek Correspondence,et al.  Guidelines Heart rate variability Standards of measurement , physiological interpretation , and clinical use Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology ( Membership of the Task Force , 2005 .

[9]  Andrzej Cichocki,et al.  Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis , 2014, IEEE Signal Processing Magazine.

[10]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[11]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .