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

arXiv: https://arxiv.org/abs/2104.12284 · HTML: https://mlsanity.com/paper/2104.12284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 17 of 17)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Multi-Scale Convolutional Neural Networks for Time Series Classification](https://mlsanity.com/paper/1603.06995/) (2016)
- [Deep Learning for Time-Series Analysis](https://mlsanity.com/paper/1701.01887/) (2017)
- [Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers](https://mlsanity.com/paper/1801.05173/) (2018)
- [Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation](https://mlsanity.com/paper/1803.04907/) (2018)
- [Insights into LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1902.10756/) (2019)
- [Convolutional Recurrent Neural Networks for Electrocardiogram Classification](https://mlsanity.com/paper/1710.06122/) (2017)
- [Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People](https://mlsanity.com/paper/2006.15940/) (2020)
- [Time Series Classification to Improve Poultry Welfare](https://mlsanity.com/paper/1811.03149/) (2018)
- [Implementation of Training Convolutional Neural Networks](https://mlsanity.com/paper/1506.01195/) (2015)

## Abstract (arXiv)

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.