# A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection

arXiv: https://arxiv.org/abs/2102.01319 · HTML: https://mlsanity.com/paper/2102.01319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach](https://mlsanity.com/paper/1812.07421/) (2018)
- [Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks](https://mlsanity.com/paper/1909.11791/) (2019)
- [A Graph-constrained Changepoint Detection Approach for ECG Segmentation](https://mlsanity.com/paper/2004.13558/) (2020)
- [A log-linear time algorithm for constrained changepoint detection](https://mlsanity.com/paper/1703.03352/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection](https://mlsanity.com/paper/2102.03538/) (2021)

## Abstract (arXiv)

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.