  Multi-modal biomedical time series (MBTS) data offers a holistic view of the
physiological state, holding significant importance in various bio-medical
applications. Owing to inherent noise and distribution gaps across different
modalities, MBTS can be complex to model. Various deep learning models have
been developed to learn representations of MBTS but still fall short in
robustness due to the ignorance of modal-to-modal variations. This paper
presents a multi-scale and multi-modal biomedical time series representation
learning (MBSL) network with contrastive learning to migrate these variations.
Firstly, MBTS is grouped based on inter-modal distances, then each group with
minimum intra-modal variations can be effectively modeled by individual
encoders. Besides, to enhance the multi-scale feature extraction (encoder),
various patch lengths and mask ratios are designed to generate tokens with
semantic information at different scales and diverse contextual perspectives
respectively. Finally, cross-modal contrastive learning is proposed to maximize
consistency among inter-modal groups, maintaining useful information and
eliminating noises. Experiments against four bio-medical applications show that
MBSL outperforms state-of-the-art models by 33.9% mean average errors (MAE) in
respiration rate, by 13.8% MAE in exercise heart rate, by 1.41% accuracy in
human activity recognition, and by 1.14% F1-score in obstructive sleep
apnea-hypopnea syndrome.
