  In this work we propose a new method for the rhythm classification of short
single-lead ECG records, using a set of high-level and clinically meaningful
features provided by the abductive interpretation of the records. These
features include morphological and rhythm-related features that are used to
build two classifiers: one that evaluates the record globally, using aggregated
values for each feature; and another one that evaluates the record as a
sequence, using a Recurrent Neural Network fed with the individual features for
each detected heartbeat. The two classifiers are finally combined using the
stacking technique, providing an answer by means of four target classes: Normal
sinus rhythm, Atrial fibrillation, Other anomaly, and Noisy. The approach has
been validated against the 2017 Physionet/CinC Challenge dataset, obtaining a
final score of 0.83 and ranking first in the competition.

Artificial Intelligence;Computer Vision and Pattern Recognition