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

Abstract: 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.

• The paper introduces a novel contrastive learning framework for biomedical time series that groups data by modality for targeted processing.
• It employs adaptive patching and multi-scale transformation to capture crucial features while maintaining computational efficiency.
• Experimental validation shows the approach outperforms state-of-the-art models in predicting physiological parameters like heart and respiration rates.

Introduction to Biomedical Time Series Learning

Biomedical time series data, which tracks physiological changes over time, is critical for understanding health states and diagnosing diseases. Harnessing this data, deep learning models can uncover patterns that indicate a patient's condition. However, these models often face robustness issues due to variations between different types of data, known as modalities.

Addressing Multi-Modal Challenges

To tackle the challenges associated with multi-modal biomedical time series (MBTS), a novel approach uses contrastive learning for effective representation. In traditional methods, variations between modalities, such as differences in the scale of measurements, can affect the model's accuracy. This paper proposes a strategy that groups MBTS based on similarities in modality distributions, allowing for targeted processing with individual encoders customized for each group.

Enhancing Feature Extraction

Another layer of complexity is added by the need to extract meaningful patterns from MBTS at various scales. Previous attempts require extensive computation and memory, or they risk losing valuable information. The proposed model introduces an efficient, multi-scale data transformation with adaptive patching and masking strategies. These techniques ensure that important features are captured at all relevant scales while maintaining computational efficiency.

Contrastive Learning for Modal Consistency

Contrastive learning, a technique for learning representations by comparing different data instances, is further refined in this model to focus on cross-modal learning. Instead of relying on augmentations that might distort MBTS, the approach guarantees constructing meaningful pairs from different but related modalities. This strategy enhances the model's ability to learn representations that are invariant to modality-specific differences while focusing on shared physiological information.

Experimental Validation

The performance of this new model outshines current state-of-the-art models in a range of biomedical tasks, including predicting respiration rates and heart rates during exercise. Ablation studies also validate the distinct contributions of inter-modal grouping, multi-scale transformation, and cross-modal contrastive learning. This affirms the approach as both effective and robust.

Conclusion

By successfully addressing modal variation and capturing complex temporal dynamics within MBTS, the proposed model advances the field of bio-medical analysis. These improvements could enable more accurate predictions and diagnoses from physiological data, demonstrating the potential of deep learning in healthcare.

The study provides a route toward more robust and capable biomedical time series learning models. With advancements like these, the future of healthcare data analysis is looking increasingly precise, personalized, and powerful.