A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection
Abstract
This study presents a new viewpoint on ECG signal analysis by applying a graph-based changepoint detection model to locate R-peak positions. This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data. The proposed learning algorithm starts with a simple initial graph and iteratively edits the graph so that the final graph has the maximum accuracy in R-peak detection. We evaluate the performance of the algorithm on the MIT-BIH Arrhythmia Database. The evaluation results demonstrate that the proposed method can obtain comparable results to other state-of-the-art approaches. The proposed method achieves the overall sensitivity of Sen = 99.64%, positive predictivity of PPR = 99.71%, and detection error rate of DER = 0.19.
Cite
@article{arxiv.2102.01319,
title = {A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection},
author = {Atiyeh Fotoohinasab and Toby Hocking and Fatemeh Afghah},
journal= {arXiv preprint arXiv:2102.01319},
year = {2021}
}
Comments
accepted in Asilomar 2020 conference