Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data

TL;DR

This study introduces a selective data augmentation method for fully convolutional networks that improves electrocardiogram classification accuracy by focusing on low-confidence samples, demonstrating significant performance gains over baseline models.

Contribution

The paper presents a novel selective augmentation approach that enhances model sensitivity by augmenting only low-confidence samples, tailored for ECG data classification.

Findings

Achieved 90% accuracy in myocardial infarction classification, outperforming the baseline's 82%.

Optimal performance was observed near a specific confidence threshold for sample selection.

Selective augmentation of low-confidence samples improves model sensitivity and overall accuracy.

Abstract

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.