Transfer Learning for Automatic Sleep Staging Using a Pre-Gelled Electrode Grid
Fabian A Radke1, Carlos F da Silva Souto1, Wiebke Pätzold1
1Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg Branch for Hearing, Speech and Audio Technology HSA, 26129 Oldenburg, Germany.
Diagnostics (Basel, Switzerland)
|May 11, 2024
Summary
This study introduces a novel method for automatic sleep phase detection using limited data from new home monitoring sensors. By pre-training on existing sleep data and fine-tuning on new sensor data, accurate sleep analysis is achieved, advancing sleep medicine.
Area of Science:
- Biomedical Engineering
- Sleep Medicine
- Artificial Intelligence
Background:
- Novel sensor solutions for home sleep monitoring offer potential for continuous observation and new insights.
- Automatic evaluation of data from new sensors is crucial due to differences from classical polysomnography (PSG).
- Limited datasets from new sensor technologies hinder the training of automatic algorithms.
Purpose of the Study:
- To develop an automatic sleep phase detection method for new sensor technologies with limited training data.
- To circumvent high system-specific training data requirements using pre-training and finetuning.
- To enable automatic sleep phase detection for small test series of novel sensor data.
Main Methods:
- Employed pre-training on large, publicly available polysomnography (PSG) datasets.
- Utilized finetuning on a small dataset (12 nights) from a new sensor technology (pre-gelled electrode grid).
- Captured electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) data; analysis focused on EEG and EOG.
Main Results:
- Achieved an overall F1 score of 0.81 for automatic sleep phase detection.
- Specific F1 scores: wake 0.84, N1 0.62, N2 0.81, N3 0.87, REM 0.88.
- Considered spatial channel distribution and approximated classical electrode positions.
Conclusions:
- Pre-training and finetuning enable accurate automatic sleep phase detection even with small datasets from new sensors.
- The developed method is effective for analyzing data from novel home sleep monitoring technologies.
- This approach facilitates advancements in sleep medicine through accessible and automated sleep analysis.
