  Contrastive representation learning is crucial in medical time series
analysis as it alleviates dependency on labor-intensive, domain-specific, and
scarce expert annotations. However, existing contrastive learning methods
primarily focus on one single data level, which fails to fully exploit the
intricate nature of medical time series. To address this issue, we present
COMET, an innovative hierarchical framework that leverages data consistencies
at all inherent levels in medical time series. Our meticulously designed model
systematically captures data consistency from four potential levels:
observation, sample, trial, and patient levels. By developing contrastive loss
at multiple levels, we can learn effective representations that preserve
comprehensive data consistency, maximizing information utilization in a
self-supervised manner. We conduct experiments in the challenging
patient-independent setting. We compare COMET against six baselines using three
diverse datasets, which include ECG signals for myocardial infarction and EEG
signals for Alzheimer's and Parkinson's diseases. The results demonstrate that
COMET consistently outperforms all baselines, particularly in setup with 10%
and 1% labeled data fractions across all datasets. These results underscore the
significant impact of our framework in advancing contrastive representation
learning techniques for medical time series. The source code is available at
https://github.com/DL4mHealth/COMET.
