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Objective: This work aims at providing a new method for the automaticdetection of atrial fibrillation, other arrhythmia and noise on short singlelead ECG signals, emphasizing the importance of the interpretability of theclassification results.  Approach: A morphological and rhythm description of the cardiac behavior isobtained by a knowledge-based interpretation of the signal using the\textit{Construe} abductive framework. Then, a set of meaningful features areextracted for each individual heartbeat and as a summary of the full record.The feature distributions were used to elucidate the expert criteria underlyingthe labeling of the 2017 Physionet/CinC Challenge dataset, enabling a manualpartial relabeling to improve the consistency of the classification rules.Finally, state-of-the-art machine learning methods are combined to provide ananswer on the basis of the feature values.  Main results: The proposal tied for the first place in the official stage ofthe Challenge, with a combined $F_1$ score of 0.83, and was even improved inthe follow-up stage to 0.85 with a significant simplification of the model.  Significance: This approach demonstrates the potential of \textit{Construe}to provide robust and valuable descriptions of temporal data even withsignificant amounts of noise and artifacts. Also, we discuss the importance ofa consistent classification criteria in manually labeled training datasets, andthe fundamental advantages of knowledge-based approaches to formalize andvalidate that criteria.

Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification