  Machine learning shows great performance in various problems of
electrocardiography (ECG) signal analysis. However, collecting a dataset for
biomedical engineering is a very difficult task. Any dataset for ECG processing
contains from 100 to 10,000 times fewer cases than datasets for image or text
analysis. This issue is especially important because of physiological phenomena
that can significantly change the morphology of heartbeats in ECG signals. In
this preliminary study, we analyze the effects of lead choice from the standard
ECG recordings, variation of ECG during 24-hours, and the effects of
QT-prolongation agents on the performance of machine learning methods for ECG
processing. We choose the problem of subject identification for analysis,
because this problem may be solved for almost any available dataset of ECG
data. In a discussion, we compare our findings with observations from other
works that use machine learning for ECG processing with different problem
statements. Our results show the importance of training dataset enrichment with
ECG signals acquired in specific physiological conditions for obtaining good
performance of ECG processing for real applications.
