一种基于图约束的变点学习的QRS波群自动检测方法
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection
摘要：
本文提出了一种基于图的变点检测模型来定位R峰位置的新观点。该模型是基于一种新的图学习算法来学习给定的标记心电数据的约束图。所提出的学习算法从一个简单的初始图开始，并迭代地编辑该图，使得最终图在R峰检测中具有最大的准确性。我们评估了该算法的性能MIT-BIH心律失常数据库。评估结果表明，该方法可以获得可比的结果，其他国家的最先进的方法。所提出的方法实现了Sen = 99.64%的总体灵敏度，PPR = 99.71%的阳性预测率，DER = 0.19的检测错误率。
英文摘要：
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.
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