Tensor-based ECG Signal Processing Applied to Atrial Fibrillation Detection


Atrial fibrillation (AF) is the most common cardiac arrhythmia, increasing the risk of a stroke substantially. Hence, early and accurate detection of AF is paramount. We present a matrix- and tensor-based method for AF detection in singleand multi-lead electrocardiogram (ECG) signals. First, the recordings are compressed into one heartbeat via the singular value decomposition (SVD). These representative heartbeats, single-lead, are collected in a matrix with modes time and recordings. In the multi-lead case, we obtain a tensor with modes lead, time and recording. By modeling the matrix (tensor) with a (multilinear) SVD, each recording, as well as new recordings, can be expressed by a coefficient vector. The comparison of a new coefficient vector with those of the model set results in morphological features, which are combined with heart rate variability information in a Support Vector Machine classifier to detect AF. The SVD-based method is tested on the 2017 PhysioNet/CinC Challenge dataset, resulting in an F1-score of 0.77. The multilinear SVD-based method is applied on the MIT-BIH AFIB and AFTDB dataset, resulting in a perfect separation. An advantage of our methods is the interpretability of the features, which is a key element in the application of automatic methods in clinical practice.

In Proceedings of the 2018 52nd Asilomar Conference on Signals, Systems, and Computers (ACSSC), Pacific Grove, CA, USA, pp. 799-805, Oct. 2018

This publication uses the Tensorlab toolbox and is based on my master thesis.

Simon Geirnaert
Simon Geirnaert
Postdoctoral researcher

My research interests include signal processing algorithm design for multi-channel biomedical sensor arrays (e.g., electroencephalography) with applications in attention decoding for brain-computer interfaces.