Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost. (24th October 2018)

• Record Type: Journal Article
• Authors: Chen, Yao; Wang, Xiao; Jung, Yonghan; Abedi, Vida; Zand, Ramin; Bikak, Marvi; Adibuzzaman, Mohammad
• Is Part Of: Physiological measurement. Volume 39:Number 10(2018:Oct.)
• Publication Date: 2018-10-24
• Subjects: atrial fibrillation -- electrocardiography -- piecewise linear function -- XGBoost; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/aadf0f
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: 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.