Fotoohinasab Atiyeh, Hocking Toby, Afghah Fatemeh
School of Informatics, Computing and Cyber Systems at Northern Arizona University, United States.
School of Informatics, Computing and Cyber Systems at Northern Arizona University, United States.
Comput Biol Med. 2021 Mar;130:104208. doi: 10.1016/j.compbiomed.2021.104208. Epub 2021 Jan 6.
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）信号是用于心血管疾病研究的最广泛使用的非侵入性工具。自动描绘心电图基准点，尤其是R波峰，是心电图处理和分析的基础。本研究提出了一种新的心电图信号分析方法，通过引入一类基于最优变化点检测模型的新型图形模型，即图约束变化点检测（GCCD）模型。GCCD模型将非平稳心电图信号中的基准点描绘视为一个变化点检测问题。所提出的模型利用变化点的稀疏性来检测心电图信号中的突变；因此，R波峰检测任务可以从任何预处理步骤中简化。在这种新方法中，关于预期变化序列的先验生物学知识通过约束图纳入模型，该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优图。最后，我们在图结构和检测准确性方面比较手动定义的图和学习到的图。我们使用MIT-BIH心律失常数据库评估该算法的性能。对于手动定义的约束图，所提出的模型实现了99.64%的总体灵敏度、99.71%的阳性预测率和0.19%的检测错误率；对于自动学习的约束图，总体灵敏度为99.76%，阳性预测率为99.68%，检测错误率为0.55%。
