  Deep learning methods have shown suitability for time series classification
in the health and medical domain, with promising results for electrocardiogram
data classification. Successful identification of myocardial infarction holds
life saving potential and any meaningful improvement upon deep learning models
in this area is of great interest. Conventionally, data augmentation methods
are applied universally to the training set when data are limited in order to
ameliorate data resolution or sample size. In the method proposed in this
study, data augmentation was not applied in the context of data scarcity.
Instead, samples that yield low confidence predictions were selectively
augmented in order to bolster the model's sensitivity to features or patterns
less strongly associated with a given class. This approach was tested for
improving the performance of a Fully Convolutional Network. The proposed
approach achieved 90 percent accuracy for classifying myocardial infarction as
opposed to 82 percent accuracy for the baseline, a marked improvement. Further,
the accuracy of the proposed approach was optimal near a defined upper
threshold for qualifying low confidence samples and decreased as this threshold
was raised to include higher confidence samples. This suggests exclusively
selecting lower confidence samples for data augmentation comes with distinct
benefits for electrocardiogram data classification with Fully Convolutional
Networks.
