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


Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
23 Aug 2023
Historique:
received: 11 07 2023
revised: 12 08 2023
accepted: 21 08 2023
medline: 11 9 2023
pubmed: 9 9 2023
entrez: 9 9 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 37687797
pii: s23177341
doi: 10.3390/s23177341
pmc: PMC10490238
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Weijia Yang (W)

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

Yuxian Wang (Y)

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

Jiancheng Hu (J)

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

Tuming Yuan (T)

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

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