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

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.