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Data Augmentation for Deep Learning-Based ECG Analysis

  • Qing PanEmail author
  • Xinyi Li
  • Luping FangEmail author
Chapter
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Abstract

Deep learning has become the technology that gets the most attention in recent years owing to its admirable performance compared to the conventional methods in a series of tasks. Though its application in electrocardiogram (ECG) analysis has enhanced the understanding and the applicability of many disease diagnosis in clinic, lack of annotated data hampers the deep learning-based ECG analysis as large amount of data is required for a well-performed deep learning model. Data augmentation, which refers to the procedure that enriches the dataset by introducing unobserved samples, plays an important role in this respect. Despite the successful usage of data augmentation in the image-based deep learning analysis, its application in one-dimensional physiological signals, such as ECG, is still limited. In this chapter, we summarize the data augmentation methods applicable for ECG analysis and examine their performance on a task for detecting atrial fibrillation (AF).

Keywords

Data augmentation Electrocardiogram Deep learning Atrial fibrillation 

Notes

Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grant 31870938) and Zhejiang Provincial Key Laboratory of Communication Networks and Applications.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Information Engineering, Zhejiang University of TechnologyHangzhouPeople’s Republic of China

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