SleepPairNet: Learning Multimodal Pediatric Sleep Representations with Random-Pair Cross-Attention

Key points

• Analyzing physiological sleep signals is important for assessing sleep quality and detecting sleep-related disorders.1
• Learning inter-modal relationships across physiological signals can provide richer representations for downstream sleep analysis.1
• We propose a random modality-pair selection strategy that exposes the model to diverse signal pairs during pretraining, enabling flexibility across modality combinations at test time.1
• Pretrained on the NCH dataset, SleepPairNet outperforms the evaluated SSL baselines on all respiratory tasks while remaining competitive on EEG-related tasks, and shows even stronger results when transferred to the external CHAT dataset.1

Sources (1)

• [1]SleepPairNet: Learning Multimodal Pediatric Sleep Representations with Random-Pair Cross-Attention
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 03:57 PM

  We introduce SleepPairNet, a multimodal self-supervised learning (SSL) framework that learns pairwise interactions between physiological signals through cross-attention.

  Analyzing physiological sleep signals is important for assessing sleep quality and detecting sleep-related disorders.

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