Abstract
STUDY OBJECTIVES: While various wearable EEG devices have been developed, performance evaluation of the devices and their associated automated sleep stage classification models is mostly limited to healthy subjects. A major barrier for applying automated wearable EEG sleep staging in clinical populations is the need for large-scale data for model training. We therefore investigated transfer learning as strategy to overcome limited data availability and optimize automated single-channel EEG sleep staging in people with sleep disorders.
METHODS: We acquired 52 single-channel frontopolar headband EEG recordings from a heterogeneous sleep-disordered population with concurrent PSG. We compared three model training strategies: 'pre-training' (i.e., training on a larger dataset of 901 conventional PSGs), 'training-from-scratch' (i.e., training on wearable headband recordings), and 'fine-tuning' (i.e., training on conventional PSGs, followed by training on headband recordings). Performance was evaluated on all headband recordings using 10-fold cross-validation.
RESULTS: Highest performance for 5-stage classification was achieved with fine-tuning (κ = .778), significantly higher than with pre-training (κ = .769) and with training-from-scratch (κ = .733). No significant differences or systematic biases were observed with clinically relevant sleep parameters derived from PSG. All sleep disorder categories showed comparable performance.
CONCLUSIONS: This study emphasizes the importance of leveraging larger available datasets through deep transfer learning to optimize performance with limited data availability. Findings indicate strong similarity in data characteristics between conventional PSG and headband recordings. Altogether, results suggest the combination of the headband, classification model, and training methodology can be viable for sleep monitoring in the heterogeneous clinical population.
Original language | English |
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Pages (from-to) | 315-323 |
Number of pages | 9 |
Journal | Journal of Clinical Sleep Medicine |
Volume | 21 |
Issue number | 2 |
Early online date | 30 Sept 2024 |
DOIs | |
Publication status | Published - 1 Feb 2025 |
Bibliographical note
© 2024 American Academy of Sleep Medicine.Keywords
- Adult
- Electroencephalography/methods
- Female
- Humans
- Male
- Middle Aged
- Polysomnography/methods
- Sleep Stages/physiology
- Sleep Wake Disorders/diagnosis
- Transfer, Psychology/physiology
- Wearable Electronic Devices
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Sleep Medicine
van Gilst, M. M. (Content manager) & van der Hout-van der Jagt, M. B. (Content manager)
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