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<p>Accurate assessment of traumatic brain injury (TBI) is critical for customization of neurorehabilitation treatments and clinical decision-making. Existing monitoring approaches either rely on subjective evaluation or isolated physiological signals, limiting real-time responsiveness and multimodal insight. This study introduces a novel framework integrating electroencephalography (EEG) and electrocardiography (ECG) to explore heart-brain synchronization as a biomarker for neurological state in TBI patients. We first define a synchronization metric using EEG delta power and heart rate variability (HRV), capturing both the degree and direction of synchronization. A two-stage contrastive learning approach is then proposed: Clinically Consistent Contrastive Learning (CCCL) leverages clinical metrics to guide positive sample selection, while Multimodal Heart-brain Contrastive Learning (MHCL) aligns synchronization features with clinical outcomes. Applied to long-term ICU recordings, the proposed approach identifies distinct synchronization patterns associated with recovery trajectories. Although the sample size (N=11) is expected to be extended, this work offers an exploratory, proof-of-concept demonstration of heart-brain synchronization as a potential real-time biomarker for neurophysiological recovery in severe TBI.</p>

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Cross-modal synchronization of EEG and ECG reveals hidden signatures of recovery in traumatic brain injury

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Xulong Li,
  • Haibo Teng,
  • Peng Chen,
  • Yuzhe Yuan,
  • Pingchun Li,
  • Mali Song,
  • Jiaxin Yu,
  • Jianguo Xu,
  • Xiangyun Li,
  • Kang Li,
  • Zhiyong Liu

摘要

Accurate assessment of traumatic brain injury (TBI) is critical for customization of neurorehabilitation treatments and clinical decision-making. Existing monitoring approaches either rely on subjective evaluation or isolated physiological signals, limiting real-time responsiveness and multimodal insight. This study introduces a novel framework integrating electroencephalography (EEG) and electrocardiography (ECG) to explore heart-brain synchronization as a biomarker for neurological state in TBI patients. We first define a synchronization metric using EEG delta power and heart rate variability (HRV), capturing both the degree and direction of synchronization. A two-stage contrastive learning approach is then proposed: Clinically Consistent Contrastive Learning (CCCL) leverages clinical metrics to guide positive sample selection, while Multimodal Heart-brain Contrastive Learning (MHCL) aligns synchronization features with clinical outcomes. Applied to long-term ICU recordings, the proposed approach identifies distinct synchronization patterns associated with recovery trajectories. Although the sample size (N=11) is expected to be extended, this work offers an exploratory, proof-of-concept demonstration of heart-brain synchronization as a potential real-time biomarker for neurophysiological recovery in severe TBI.