August 13, 2018

Ranking of the most reliable beat morphology and heart rate variability features for the detection of atrial fibrillation in short single-lead ECG

Structured PICO

Population

12,186 single-lead ECG recordings with a duration <60 s (8,528 in training set, 3,658 in hidden test set) from the 2017 PhysioNet/CinC Challenge.

Intervention

Algorithm for time domain ECG analysis extracting 44 features and using a linear discriminant analysis (LDA) classifier.

Outcome

Challenge F1 score for classification of atrial fibrillation, normal sinus rhythm, other arrhythmia, and strong noise.surrogate

A linear discriminant analysis classifier using time domain ECG features, primarily heart rate variability, achieved an overall F1 score of 0.80 for detecting atrial fibrillation and other rhythms in short single-lead ECGs.

Abstract

OBJECTIVE: This study participated in the 2017 PhysioNet/CinC Challenge dedicated to the classification of atrial fibrillation (AF), normal sinus rhythm (Normal), other arrhythmia (Other) and strong noise, using single-lead electrocardiogram (ECG) recordings with a duration 50 ms from the preceding RR interval; (2) Poincaré plot geometry estimated by the ratio of the minor-to-major semi-axes of the fitted ellipse; (3) P-wave presence in the average beat; (4) mean percentage of the RR interval first differences; and (5) mean correlation of all beats against the average beat. The global rank of feature extraction methods highlighted that HRV alone was able to provide 92.5% of the maximal F1 score (0.74 versus 0.8). The added value of more complex ECG morphology analysis was less significant for Normal, AF, and Other rhythms (+0.02 to 0.08 points) than for Noise (+0.19 points); however, these were indispensable for wearable ECG recording devices with frequent artefact disturbance.

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