Dipartimento di Informatica, Università degli Studi di Milano, 20133 Milan, Italy.
School of Nursing, University of California, San Francisco, CA 94143, USA.
Sensors (Basel). 2023 May 31;23(11):5237. doi: 10.3390/s23115237.
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.
人工智能技术最近成为分析心电图 (ECG) 的焦点。然而，基于人工智能的模型的性能依赖于大规模标记数据集的积累，这是具有挑战性的。为了提高基于人工智能的模型的性能，最近开发了数据扩充 (DA) 策略。本研究对 ECG 信号的 DA 进行了全面的系统文献回顾。我们进行了系统搜索，并按人工智能应用、涉及的导联数量、DA 方法、分类器、DA 后性能改进以及使用的数据集对选定的文献进行了分类。通过这些信息，本研究更好地了解了 ECG 扩充在提高基于人工智能的 ECG 应用性能方面的潜力。本研究遵循系统评价的严格 PRISMA 指南。为了确保全面覆盖，在 IEEE Explore、PubMed 和 Web of Science 等多个数据库中搜索了 2013 年至 2023 年期间的出版物。仔细审查记录以确定它们与研究目标的相关性，并选择符合纳入标准的记录进行进一步分析。因此，认为有 119 篇论文与进一步审查相关。总的来说，本研究揭示了 DA 在推进 ECG 诊断和监测领域的潜力。
