Abstract
The electrocardiogram (ECG) is one of the most widespread diagnostic tools in
healthcare and supports the diagnosis of cardiovascular disorders. Deep
learning methods are a successful and popular technique to detect indications
of disorders from an ECG signal. However, there are open questions around the
robustness of these methods to various factors, including physiological ECG
noise. In this study we generate clean and noisy versions of an ECG dataset
before applying Symmetric Projection Attractor Reconstruction (SPAR) and
scalogram image transformations. A pretrained convolutional neural network is
trained using transfer learning to classify these image transforms. For the
clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were
0.70 and 0.79, respectively, and the scores decreased by less than 0.05 for the
noisy ECG datasets. Notably, when the network trained on clean data was used to
classify the noisy datasets, performance decreases of up to 0.18 in F1 scores
were seen. However, when the network trained on the noisy data was used to
classify the clean dataset, the performance decrease was less than 0.05. We
conclude that physiological ECG noise impacts classification using deep
learning methods and careful consideration should be given to the inclusion of
noisy ECG signals in the training data when developing supervised networks for
ECG classification.