Research on ECG Signal Classification Based on Data Enhancement of Generative Adversarial Network

Liu, Jian; Xia, Xiaodong; Xiang, Ping; Jiang, Hui; Han, Chunyang

doi:10.1007/978-3-031-06794-5_33

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…In the context of ECG DA, the authors of [15,17,18,24,94,100,105,[107][108][109][110][111][112][114][115][116][117][118][119]122,123,126] used GAN to augment the samples of the minor classes of the MIT-BIH AD. The augmented samples were then fed to a DL model for ECG beat classification, which demonstrated a notable improvement ranging from 0.24-32% compared to the unaugmented samples.…”

Section: Deep Generative Modelsmentioning

confidence: 99%

A Systematic Survey of Data Augmentation of ECG Signals for AI Applications

Rahman

Rivolta

Badilini

et al. 2023

Sensors

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AI techniques have recently been put under the spotlight for analyzing electrocardiograms (ECGs). However, the performance of AI-based models relies on the accumulation of large-scale labeled datasets, which is challenging. To increase the performance of AI-based models, data augmentation (DA) strategies have been developed recently. The study presented a comprehensive systematic literature review of DA for ECG signals. We conducted a systematic search and categorized the selected documents by AI application, number of leads involved, DA method, classifier, performance improvements after DA, and datasets employed. With such information, this study provided a better understanding of the potential of ECG augmentation in enhancing the performance of AI-based ECG applications. This study adhered to the rigorous PRISMA guidelines for systematic reviews. To ensure comprehensive coverage, publications between 2013 and 2023 were searched across multiple databases, including IEEE Explore, PubMed, and Web of Science. The records were meticulously reviewed to determine their relevance to the study’s objective, and those that met the inclusion criteria were selected for further analysis. Consequently, 119 papers were deemed relevant for further review. Overall, this study shed light on the potential of DA to advance the field of ECG diagnosis and monitoring.

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