Electrocardiogram (ECG) analysis is the standard ofcare for the diagnosis of irregular heartbeat patterns, known as arrhythmias. This paper presents a deep learning system for the automatic detection and multilabel classification of arrhythmias in ECG recordings. Our system composes three differentiable operators: a scattering transform (ST), a depthwise separable convolutional network (DSC), and a bidirectional long short-term memory network (BiLSTM). The originality of our approach is that all three operators are implemented in Python. This is in contrast to previous publications, which pre-computed ST coefficients in MATLAB. The implementation of ST on Python was made possible by using a new software library for scattering transform named Kymatio.This paper presents the first successful application of Kymatio to the analysis of biomedical signals. As part of the PhysioNet/Computing in Cardiology Challenge 2020, we trained our hybrid Scattering–LSTM model to classify 27 cardiac arrhythmias from two databases of 12–lead ECGs: CPSC2018 and PTB-XL, comprising 32k recordings in total. Our team “BitScattered” achieved a Challenge metric of 0.536±0.012 over ten folds of cross-validation.