  An essential part for the accurate classification of electrocardiogram (ECG)
signals is the extraction of informative yet general features, which are able
to discriminate diseases. Cardiovascular abnormalities manifest themselves in
features on different time scales: small scale morphological features, such as
missing P-waves, as well as rhythmical features apparent on heart rate scales.
For this reason we incorporate a variant of the complex wavelet transform,
called a scatter transform, in a deep residual neural network (ResNet). The
former has the advantage of being derived from theory, making it well behaved
under certain transformations of the input. The latter has proven useful in ECG
classification, allowing feature extraction and classification to be learned in
an end-to-end manner. Through the incorporation of trainable layers in between
scatter transforms, the model gains the ability to combine information from
different channels, yielding more informative features for the classification
task and adapting them to the specific domain. For evaluation, we submitted our
model in the official phase in the PhysioNet/Computing in Cardiology Challenge
2020. Our (Team Triage) approach achieved a challenge validation score of
0.640, and full test score of 0.485, placing us 4th out of 41 in the official
ranking.
