Classification of short single-lead electrocardiograms (ECGs for atrial fibrillation detection using piecewise linear spline and XGBoost
Classification of short single-lead electrocardiograms (ECGs for atrial fibrillation detection using piecewise linear spline and XGBoost
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DOI：
10.1088/1361-6579/aadf0f
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发表时间：
2018-10-01
影响因子：
3.2
通讯作者：
Adibuzzaman, Mohammad
中科院分区：
文献类型：
--
作者：
Chen, Yao;Wang, Xiao;Adibuzzaman, Mohammad
目的：房颤的检测对卒中危险分层有重要意义。我们开发了一种新的方法，将心电图（ecg）分为正常、心房颤动和2017年PhysioNet挑战赛定义的其他心律失常。方法：更具体地说，我们使用分段线性样条进行特征选择，并使用梯度增强算法进行分类器。该算法通过分段线性样条对心电波形进行拟合，提取与分段线性样条系数相关的形态学特征。使用XGBoost对形态学系数和心率变异性特征进行分类。主要结果：通过PhysioNet Challenge数据库（专家分类的3658张心电图）对算法的性能进行评价。我们的算法在10倍交叉验证中平均F-1得分达到81%，在独立测试集上F-1得分也达到81%。这个分数与2017年PhysioNet挑战赛官方阶段的前9名分数（81%）相似。意义：该算法在选择形态学特征的多标签短心电分类中表现出良好的性能。
Objective: Detection of atrial fibrillation is important for risk stratification of stroke. We developed a novel methodology to classify electrocardiograms (ECGs) to normal, atrial fibrillation and other cardiac dysrhythmias as defined by the PhysioNet Challenge 2017 . Approach: More specifically, we used piecewise linear splines for the feature selection and a gradient boosting algorithm for the classifier. In the algorithm, the ECG waveform is fitted by a piecewise linear spline, and morphological features relating to the piecewise linear spline coefficients are extracted. XGBoost is used to classify the morphological coefficients and heart rate variability features. Main results: The performance of the algorithm was evaluated by the PhysioNet Challenge database (3658 ECGs classified by experts). Our algorithm achieved an average F-1 score of 81% for a 10-fold cross-validation and also achieved 81% for F-1 score on the independent testing set. This score is similar to the top 9th score (81%) in the official phase of the PhysioNet Challenge 2017. Significance: Our algorithm presents a good performance on multi-label short ECG classification with selected morphological features.