Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches

Deep transfer learning for automated single-lead EEG sleep staging with channel and population mismatches

Introduction: Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice. However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, s...

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Bibliographic Details
Main Authors: Jaap F. Van Der Aar, Daan A. Van Den Ende, Pedro Fonseca, Fokke B. Van Meulen, Sebastiaan Overeem, Merel M. Van Gilst, Elisabetta Peri
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Physiology
Subjects:
polysomnography
sleep staging
single-channel
wearable EEG
fine-tuning
deep learning
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2023.1287342/full

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