  Holter monitoring, a long-term ECG recording (24-hours and more), contains a
large amount of valuable diagnostic information about the patient. Its
interpretation becomes a difficult and time-consuming task for the doctor who
analyzes them because every heartbeat needs to be classified, thus requiring
highly accurate methods for automatic interpretation. In this paper, we present
a three-stage process for analysing Holter recordings with robustness to noisy
signal. First stage is a segmentation neural network (NN) with encoderdecoder
architecture which detects positions of heartbeats. Second stage is a
classification NN which will classify heartbeats as wide or narrow. Third stage
in gradient boosting decision trees (GBDT) on top of NN features that
incorporates patient-wise features and further increases performance of our
approach. As a part of this work we acquired 5095 Holter recordings of patients
annotated by an experienced cardiologist. A committee of three cardiologists
served as a ground truth annotators for the 291 examples in the test set. We
show that the proposed method outperforms the selected baselines, including two
commercial-grade software packages and some methods previously published in the
literature.
