Lim Jeonghwa, Lee Yeha, Jang Wonseuk, Joo Sunghoon
VUNO Inc, 9F, 479, Gangnam-daero, Seocho-gu, Seoul, 06541 Republic of Korea.
Department of Medical Device Engineering and Management, Yonsei University College of Medicine, Seoul, Republic of Korea.
Biomed Eng Lett. 2025 Jan 8;15(2):377-388. doi: 10.1007/s13534-024-00455-3. eCollection 2025 Mar.
Deep learning has demonstrated remarkable performance across various domains. One of the techniques contributing to this success is data augmentation. The essence of data augmentation lies in synthesizing data while preserving accurate labels. In this research, we introduce a data augmentation technique optimized for electrocardiogram (ECG) data by focusing on the unique angles between precordial leads in 12-lead ECG, considering situations that may occur in a clinical environment. Subsequently, we utilize the proposed data augmentation technique to train a deep learning model for diagnosing atrial fibrillation or atrial flutter, generalized supraventricular tachycardia, first-degree atrioventricular block, left bundle branch block and myocardial infarction from ECG signals, and evaluate its performance to validate the effectiveness of the proposed method. Compared to other data augmentation methods, our approach demonstrated improved performance across various datasets and most tasks, thereby showcasing its potential to enhance diagnostic accuracy. Additionally, our method is simple to implement, offering a gain in total training time compared to other augmentation methods. This study holds the potential to positively advance further development in the fields of bio-signal processing and deep learning technology, addressing the issue of the lack of optimized data augmentation techniques applicable to ECG data in the future.
The online version contains supplementary material available at 10.1007/s13534-024-00455-3.
深度学习在各个领域都展现出了卓越的性能。促成这一成功的技术之一是数据增强。数据增强的本质在于在保留准确标签的同时合成数据。在本研究中，我们通过关注12导联心电图中胸前导联之间的独特角度，考虑临床环境中可能出现的情况，引入了一种针对心电图（ECG）数据优化的数据增强技术。随后，我们利用所提出的数据增强技术训练一个深度学习模型，用于从心电图信号中诊断心房颤动或心房扑动、广义室上性心动过速、一度房室传导阻滞、左束支传导阻滞和心肌梗死，并评估其性能以验证所提方法的有效性。与其他数据增强方法相比，我们的方法在各种数据集和大多数任务中都表现出了更高的性能，从而展示了其提高诊断准确性的潜力。此外，我们的方法易于实现，与其他增强方法相比，在总训练时间上有所缩短。这项研究有可能积极推动生物信号处理和深度学习技术领域的进一步发展，解决未来缺乏适用于心电图数据的优化数据增强技术的问题。
在线版本包含可在10.1007/s13534-024-00455-3获取的补充材料。
