Deep learning models for electrocardiogram (ECG) classification can be affected by the presence of physiological

noise on the ECG, as shown in previous work. In this study, we explore the impact of different physiological noise types, and differing signal-to-noise ratios (SNRs) of noise on classification performance. We find that classification performance is impacted differently by different noise types. In addition, the best classification performance comes from using a network trained on clean ECGs to classify clean ECGs. In conclusion, this study has revealed several questions regarding inclusion or exclusion of noise on the ECG for training and classification by deep learning models.