  In this work we apply variations of ResNet architecture to the task of atrial
fibrillation classification. Variations differ in number of filter after first
convolution, ResNet block layout, number of filters in block convolutions and
number of ResNet blocks between downsampling operations. We have found a range
of model size in which models with quite different configurations show similar
performance. It is likely that overall number of parameters plays dominant role
in model performance. However, configuration parameters like layout have values
that constantly lead to better results, which allows to suggest that these
parameters should be defined and fixed in the first place, while others may be
varied in a reasonable range to satisfy any existing constraints.
