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Medical time series data are indispensable in healthcare, providing criticalinsights for disease diagnosis, treatment planning, and patient management. Theexponential growth in data complexity, driven by advanced sensor technologies,has presented challenges related to data labeling. Self-supervised learning(SSL) has emerged as a transformative approach to address these challenges,eliminating the need for extensive human annotation. In this study, weintroduce a novel framework for Medical Time Series Representation Learning,known as MTS-LOF. MTS-LOF leverages the strengths of contrastive learning andMasked Autoencoder (MAE) methods, offering a unique approach to representationlearning for medical time series data. By combining these techniques, MTS-LOFenhances the potential of healthcare applications by providing moresophisticated, context-rich representations. Additionally, MTS-LOF employs amulti-masking strategy to facilitate occlusion-invariant feature learning. Thisapproach allows the model to create multiple views of the data by maskingportions of it. By minimizing the discrepancy between the representations ofthese masked patches and the fully visible patches, MTS-LOF learns to capturerich contextual information within medical time series datasets. The results ofexperiments conducted on diverse medical time series datasets demonstrate thesuperiority of MTS-LOF over other methods. These findings hold promise forsignificantly enhancing healthcare applications by improving representationlearning. Furthermore, our work delves into the integration of joint-embeddingSSL and MAE techniques, shedding light on the intricate interplay betweentemporal and structural dependencies in healthcare data. This understanding iscrucial, as it allows us to grasp the complexities of healthcare data analysis.

MTS-LOF: Medical Time-Series Representation Learning via Occlusion-Invariant Features