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

DevFeed: [SleepPairNet: Learning Multimodal Pediatric Sleep Representations with Random-Pair Cross-Attention](<https://devfeed.tech/articles/sleeppairnet-learning-multimodal-pediatric-sleep-representations-with-random-pair-cross-attention-93650.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2610.26628>)

Author: Saurav Raj Pandey, Harlin Lee

Published: 2026-10-21T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Representation learning](<https://devfeed.tech/topics/representation-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>)

Tags: [abstract](<https://devfeed.tech/tags/abstract.md>), [all](<https://devfeed.tech/tags/all.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [attention](<https://devfeed.tech/tags/attention.md>), [can](<https://devfeed.tech/tags/can.md>), [chat](<https://devfeed.tech/tags/chat.md>), [cross](<https://devfeed.tech/tags/cross.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [eeg](<https://devfeed.tech/tags/eeg.md>), [external](<https://devfeed.tech/tags/external.md>), [flexibility](<https://devfeed.tech/tags/flexibility.md>), [framework](<https://devfeed.tech/tags/framework.md>), [important](<https://devfeed.tech/tags/important.md>), [learning](<https://devfeed.tech/tags/learning.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [pair](<https://devfeed.tech/tags/pair.md>), [random](<https://devfeed.tech/tags/random.md>), [sleep](<https://devfeed.tech/tags/sleep.md>), [with](<https://devfeed.tech/tags/with.md>)

## AI overview

SleepPairNet learns multimodal pediatric sleep representations by using cross-attention to model pairwise interactions between physiological signals. During self-supervised pretraining, it randomly selects modality pairs, exposing the model to varied combinations and allowing flexibility across modalities at test time. Pretrained on the NCH dataset, it outperforms the evaluated self-supervised baselines on all respiratory tasks and remains competitive on EEG-related tasks; transfer to the external CHAT dataset yields stronger results. The excerpt does not specify metrics, datasets' characteristics, or which baselines and modality combinations were evaluated.

## Source excerpt

arXiv:2610.26628v1 Announce Type: new Abstract: Analyzing physiological sleep signals is important for assessing sleep quality and detecting sleep-related disorders. Learning inter-modal relationships across physiological signals can provide richer representations for downstream sleep analysis. We introduce SleepPairNet, a multimodal self-supervised learning (SSL) framework that learns pairwise interactions between physiological signals through cross-attention. 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. 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.