PhysioME: A Robust Multimodal Self-Supervised Framework for Physiological Signals with Missing Modalities

2025/10/13 by Cheol-Hui Lee, Hwa-Yeon Lee, Lee, Cheol-Hui +5
Computer Science · Medicine · Neuroscience · #Artificial Intelligence (cs.AI) #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.2510.11110

openalex publication_date 2025/10/13 · openalex created_date 2025/10/15 · openalex updated_date 2026/07/28

Abstract

Missing or corrupted modalities are common in physiological signal-based medical applications owing to hardware constraints or motion artifacts. However, most existing methods assume the availability of all modalities, resulting in substantial performance degradation in the absence of any modality. To overcome this limitation, this study proposes PhysioME, a robust framework designed to ensure reliable performance under missing modality conditions. PhysioME adopts: (1) a multimodal self-supervised learning approach that combines contrastive learning with masked prediction; (2) a Dual-PathNeuroNet backbone tailored to capture the temporal dynamics of each physiological signal modality; and (3) a restoration decoder that reconstructs missing modality tokens, enabling flexible processing of incomplete inputs. The experimental results show that PhysioME achieves high consistency and generalization performance across various missing modality scenarios. These findings highlight the potential of PhysioME as a reliable tool for supporting clinical decision-making in real-world settings with imperfect data availability.

Citations

• Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
• Deep Multimodal Learning with Missing Modality: A Survey
• CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
• Towards Good Practices for Missing Modality Robust Action Recognition
• ReLearn: A Robust Machine Learning Framework in Presence of Missing Data\n for Multimodal Stress Detection from Physiological Signals
• Deep Learning for Spatio-Temporal Data Mining: A Survey
• Deep Learning for Spatio-Temporal Data Mining: A Survey
• Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG

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