CLHi-MTS: A Contrastive Learning-Based Hierarchical Framework for Masked Medical Time-Series Modeling

Medical time-series analysis is crucial for the diagnosis and treatment of various diseases. As modern medical sensors evolve, the complexity and dimensionality of medical signals have increased, posing challenges for data labeling and classification. Self-supervised learning has emerged as a promising solution by automatically extracting meaningful feature representations from raw data without manually annotated labels. However, existing research has often focused on the time-point or instance level input and overlook the intricate relationships among different channels, which are crucial for precise data representation. In this study, we propose CLHi-MTS, a novel Contrastive Learning-Based Hierarchical Framework for Masked Medical Time-Series Modeling that leverages three distinct levels of proximity: channel interdependencies, temporal fluctuations, and instance-level correlations, to guide the reconstruction of masked segments enhanced with contrastive learning. We also introduce a new augmentation strategy for generating positive examples in contrastive learning, further enhancing the model’s representation learning ability. Evaluated on five datasets for both in-domain and cross-domain medical time-series classification tasks, our framework outperforms six state-of-the-art self-supervised methods.