U-Net Architecture for the Automatic Detection and Delineation of the Electrocardiogram

Abstract

Automatic detection and delineation of the electrocardiogram (ECG) is usually the first step for many feature extraction tasks. Although deep learning (DL) approaches have been proposed in the literature, those employ non-optimal and outdated architectures. Thus, rule-based algorithms remain as state-of-the-art. Nevertheless, those may not generalize on other datasets and require difficult offline tuning for adapting to new scenarios. This work frames this task as a segmentation problem for using an adaptation of the U-Net architecture, a fully convolutional network. The detection performance shows a precision of 89.27%, 98.18% and 93.60% for the detection of the P, QRS and T waves, respectively, and a recall of 89.07%, 99.47% and 95.21%. This work shows promising results, outperforming existing DL approaches while being more generalizable than rule-based methods.