  We propose an algorithm for electrocardiogram (ECG) segmentation using a
UNet-like full-convolutional neural network. The algorithm receives an
arbitrary sampling rate ECG signal as an input, and gives a list of onsets and
offsets of P and T waves and QRS complexes as output. Our method of
segmentation differs from others in speed, a small number of parameters and a
good generalization: it is adaptive to different sampling rates and it is
generalized to various types of ECG monitors. The proposed approach is superior
to other state-of-the-art segmentation methods in terms of quality. In
particular, F1-measures for detection of onsets and offsets of P and T waves
and for QRS-complexes are at least 97.8%, 99.5%, and 99.9%, respectively.
