Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review.

ABSTRACT: 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 neg

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10181273 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature