Paper Info
Title
Tensor-basedECG Signal Processing Applied to Atrial Fibrillation Detection.
Abstract
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 single-and 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 F 1 -score of 0.77. The multilinear SVD-based method is applied on the MIT-BIH AF IB and AF TDB 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.
Year
DOI
Venue
2018
10.1109/ACSSC.2018.8645441
ACSSC
Field
DocType
Citations 
Singular value decomposition,Interpretability,Signal processing,Heartbeat,Tensor,Pattern recognition,Matrix (mathematics),Computer science,Cardiac arrhythmia,Electronic engineering,Artificial intelligence,Multilinear map
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Simon Geirnaert100.34
Griet Goovaerts243.89
Padhy, S.3163.29
Martijn Bousse400.68
Lieven De Lathauwer53002226.72
Sabine Van Huffel61058149.38