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An essential part for the accurate classification of electrocardiogram (ECG)signals is the extraction of informative yet general features, which are ableto discriminate diseases. Cardiovascular abnormalities manifest themselves infeatures on different time scales: small scale morphological features, such asmissing 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). Theformer has the advantage of being derived from theory, making it well behavedunder certain transformations of the input. The latter has proven useful in ECGclassification, allowing feature extraction and classification to be learned inan end-to-end manner. Through the incorporation of trainable layers in betweenscatter transforms, the model gains the ability to combine information fromdifferent channels, yielding more informative features for the classificationtask and adapting them to the specific domain. For evaluation, we submitted ourmodel in the official phase in the PhysioNet/Computing in Cardiology Challenge2020. Our (Team Triage) approach achieved a challenge validation score of0.640, and full test score of 0.485, placing us 4th out of 41 in the officialranking.

Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification