Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features

Detecting atrial fibrillation (AF) from electrocardiogram (ECG) recordings remains a challenging task. In this paper, a new AF detection method was proposed to classify the ECG recordings into one of four classes: Normal rhythm, AF, Other rhythm, and Noisy recordings.

The proposed method comprised preprocessing, feature extraction, and classification. In preprocessing, R-peaks were detected, and RR intervals and delta RR intervals were extracted. In feature extraction, 30 multi-level features were extracted, including AF features (n  =  4), morphology features (n  =  20), RR interval features (n  =  2), and features of similarity index between beats (n  =  4). In classification, these features were used to train an AdaBoosted decision tree ensemble for classifying ECG recordings into the four classes. The decision tree ensemble was trained with 100-fold cross-validation on the training dataset (n  =  8528) provided by the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge.

The trained classifier was submitted to the Challenge for testing on the unavailable test dataset (n  =  3658); the official F ₁ scores for 'Normal', 'AF', 'Other' were 0.91, 0.82, and 0.73, respectively, and the overall F ₁ score was 0.82 (ranking equal 5th with eight other algorithms in the 2017 PhysioNet/CinC Challenge).

The proposed algorithm may be used as a new method for AF detection.