Objective: This work aims at providing a new method for the automatic
detection of atrial fibrillation, other arrhythmia and noise on short single
lead ECG signals, emphasizing the importance of the interpretability of the
classification results.
Approach: A morphological and rhythm description of the cardiac behavior is
obtained by a knowledge-based interpretation of the signal using the
\textit{Construe} abductive framework. Then, a set of meaningful features are
extracted for each individual heartbeat and as a summary of the full record.
The feature distributions were used to elucidate the expert criteria underlying
the labeling of the 2017 Physionet/CinC Challenge dataset, enabling a manual
partial relabeling to improve the consistency of the classification rules.
Finally, state-of-the-art machine learning methods are combined to provide an
answer on the basis of the feature values.
Main results: The proposal tied for the first place in the official stage of
the Challenge, with a combined $F_1$ score of 0.83, and was even improved in
the 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 with
significant amounts of noise and artifacts. Also, we discuss the importance of
a consistent classification criteria in manually labeled training datasets, and
the fundamental advantages of knowledge-based approaches to formalize and
validate that criteria.

本研究提出了一种基于 Construe 推理框架的心电信号自动检测房颤，其他心律失常和噪音的新方法，并探讨了一致的分类标准在训练数据集中的重要性，研究结果表明，使用该方法可以对包含较大噪声水平的数据进行有价值的描述。