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睡眠 CLIP：一种基于睡眠信号和睡眠分期标签的多模态睡眠分期模型。

Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels.

机构信息

School of Applied Mathematics, Chengdu University of Information Science and Technology, Chengdu 610051, China.

出版信息

Sensors (Basel). 2023 Aug 23;23(17):7341. doi: 10.3390/s23177341.

DOI:10.3390/s23177341
PMID:37687797
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10490238/
Abstract

Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively.

摘要

自 OpenAI 团队设计的对比语言-图像预训练 (CLIP) 模型发布以来，由于其准确性高，它已被应用于多个领域。睡眠分期是诊断睡眠障碍的重要方法，而高精度地完成睡眠分期任务一直是睡眠分期算法设计人员的主要目标。本研究旨在设计一种基于 CLIP 模型的多模态模型，该模型更适合使用睡眠信号和标签的睡眠分期任务。模型的预训练工作涉及五个不同的训练集。最后，该方法在两个训练集（EDF-39 和 EDF-153）上进行了测试，准确率分别为 87.3%和 85.4%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d50/10490238/6b7212089a4c/sensors-23-07341-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d50/10490238/6b7212089a4c/sensors-23-07341-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d50/10490238/175f3448413b/sensors-23-07341-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d50/10490238/69289c12f066/sensors-23-07341-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d50/10490238/6b7212089a4c/sensors-23-07341-g005.jpg

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