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Multi-modal biomedical time series (MBTS) data offers a holistic view of thephysiological state, holding significant importance in various bio-medicalapplications. Owing to inherent noise and distribution gaps across differentmodalities, MBTS can be complex to model. Various deep learning models havebeen developed to learn representations of MBTS but still fall short inrobustness due to the ignorance of modal-to-modal variations. This paperpresents a multi-scale and multi-modal biomedical time series representationlearning (MBSL) network with contrastive learning to migrate these variations.Firstly, MBTS is grouped based on inter-modal distances, then each group withminimum intra-modal variations can be effectively modeled by individualencoders. Besides, to enhance the multi-scale feature extraction (encoder),various patch lengths and mask ratios are designed to generate tokens withsemantic information at different scales and diverse contextual perspectivesrespectively. Finally, cross-modal contrastive learning is proposed to maximizeconsistency among inter-modal groups, maintaining useful information andeliminating noises. Experiments against four bio-medical applications show thatMBSL outperforms state-of-the-art models by 33.9% mean average errors (MAE) inrespiration rate, by 13.8% MAE in exercise heart rate, by 1.41% accuracy inhuman activity recognition, and by 1.14% F1-score in obstructive sleepapnea-hypopnea syndrome.

Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series