A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection
Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah

TL;DR
This paper introduces a novel graph-based changepoint detection method for automatic QRS complex detection in ECG signals, achieving high accuracy without extensive preprocessing.
Contribution
It proposes a new GCCD model that incorporates biological knowledge via constraint graphs and a greedy graph learning algorithm for improved R-peak detection.
Findings
Achieves over 99.7% sensitivity and positive predictivity.
Demonstrates effective automatic graph learning for ECG analysis.
Outperforms traditional methods in detection accuracy.
Abstract
The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. Automatic delineation of ECG fiducial points, in particular the R-peak, serves as the basis for ECG processing and analysis. This study proposes a new method of ECG signal analysis by introducing a new class of graphical models based on optimal changepoint detection models, named the graph-constrained changepoint detection (GCCD) model. The GCCD model treats fiducial points delineation in the non-stationary ECG signal as a changepoint detection problem. The proposed model exploits the sparsity of changepoints to detect abrupt changes within the ECG signal; thereby, the R-peak detection task can be relaxed from any preprocessing step. In this novel approach, prior biological knowledge about the expected sequence of changes is incorporated into the model using the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
