医疗时间序列是随着时间的推移收集的顺序数据,用于衡量与健康相关的信号,如脑电图(EEG),心电图(ECG),和重症监护病房(ICU)的读数。分析医疗时间序列并确定潜在的模式和趋势,从而发现高度有价值的见解,以增强诊断,治疗,风险评估,和疾病进展。然而,医学时间序列中的数据挖掘受到耗时耗力的样本标注的严重限制,和专家依赖。为了缓解这一挑战,新兴的自监督对比学习,自2020年以来取得了巨大的成功,是一个有前途的解决方案。对比学习旨在通过对比正样本和负样本来学习代表性嵌入,而无需显式标签。这里,我们根据PRISMA标准,对对比学习如何缓解医学时间序列中的标签缺乏进行了系统评价.我们在五个科学数据库(IEEE,ACM,Scopus,谷歌学者,和PubMed),并根据纳入标准检索了1908年的论文。应用排除标准后,和标题筛选,abstract,和全文水平,我们仔细审查了这方面的43篇论文。具体来说,本文概述了对比学习的流水线,包括训练前,微调,和测试。我们提供了应用于医疗时间序列数据的各种增强的全面总结,预训练编码器的架构,微调分类器和聚类的类型,和流行的对比损失函数。此外,我们概述了医疗时间序列中使用的不同数据类型,突出感兴趣的医学应用,并提供已在该领域使用的51个公共数据集的综合表格。此外,本文将对未来有前途的范围进行讨论,例如为有效的增强设计提供指导,开发一个分析分层时间序列的统一框架,和研究处理多模态数据的方法。尽管处于早期阶段,自我监督对比学习在克服医学时间序列研究中对专家创建注释的需求方面显示出巨大的潜力。
Medical time series are sequential data collected over time that measures health-related signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive care unit (ICU) readings. Analyzing medical time series and identifying the latent patterns and trends that lead to uncovering highly valuable insights for enhancing diagnosis, treatment, risk assessment, and disease progression. However, data mining in medical time series is heavily limited by the sample annotation which is time-consuming and labor-intensive, and expert-depending. To mitigate this challenge, the emerging self-supervised contrastive learning, which has shown great success since 2020, is a promising solution. Contrastive learning aims to learn representative embeddings by contrasting positive and negative samples without the requirement for explicit labels. Here, we conducted a systematic
review of how contrastive learning alleviates the label scarcity in medical time series based on PRISMA standards. We searched the studies in five scientific databases (IEEE, ACM, Scopus, Google Scholar, and PubMed) and retrieved 1908 papers based on the inclusion criteria. After applying excluding criteria, and screening at title, abstract, and full text levels, we carefully reviewed 43 papers in this area. Specifically, this paper outlines the pipeline of contrastive learning, including pre-training, fine-tuning, and testing. We provide a comprehensive summary of the various augmentations applied to medical time series data, the architectures of pre-training encoders, the types of fine-tuning classifiers and clusters, and the popular contrastive loss functions. Moreover, we present an overview of the different data types used in medical time series, highlight the medical applications of interest, and provide a comprehensive table of 51 public datasets that have been utilized in this field. In addition, this paper will provide a discussion on the promising future scopes such as providing guidance for effective augmentation design, developing a unified framework for analyzing hierarchical time series, and investigating methods for processing multimodal data. Despite being in its early stages, self-supervised contrastive learning has shown great potential in overcoming the need for expert-created annotations in the research of medical time series.