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

ABSTRACT: 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, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). Methods: We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset

SUBMITTER: Van Der Aar JF 

PROVIDER: S-EPMC10796543 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature