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 constraint graph, which can be defined manually or automatically. First, we define the constraint graph manually; then, we present a graph learning algorithm that can search for an optimal graph in a greedy scheme. Finally, we compare the manually defined graphs and learned graphs in terms of graph structure and detection accuracy. We evaluate the performance of the algorithm using the MIT-BIH Arrhythmia Database. The proposed model achieves an overall sensitivity of 99.64%, positive predictivity of 99.71%, and detection error rate of 0.19 for the manually defined constraint graph and overall sensitivity of 99.76%, positive predictivity of 99.68%, and detection error rate of 0.55 for the automatic learning constraint graph.

心电图（ECG）信号是用于研究心血管疾病的最广泛使用的非侵入性工具。自动描绘ECG基准点，尤其是R峰，是ECG处理和分析的基础。本研究通过引入基于最佳变化点检测模型的新型图形模型（称为图约束变化点检测（GCCD）模型），提出了一种新的ECG信号分析方法。GCCD模型将非平稳ECG信号中的基准点描绘视为变化点检测问题。所提出的模型利用变化点的稀疏性来检测ECG信号内的突变。因此，可以从任何预处理步骤中放松R峰检测任务。在这种新颖的方法中，使用约束图将有关预期的变化序列的先验生物学知识合并到模型中，约束图可以手动或自动定义。首先，我们手动定义约束图；然后，我们提出一种图学习算法，该算法可以在贪婪方案中搜索最佳图。最后，我们根据图的结构和检测精度比较手动定义的图和学习的图。我们使用MIT-BIH心律失常数据库评估算法的性能。对于手动定义的约束图，该模型的总体灵敏度为99.64％，阳性预测率为99.71％，检测错误率为0.19；对于人工约束条件图，该模型的总体灵敏度为99.76％，阳性预测率为95.68％，检测误差率为0.55。自动学习约束图。