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 matrixand 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 F_i-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.