Polysomnography (PSG) is an indispensable diagnostic tool in sleep medicine, essential for identifying various sleep disorders. By capturing physiological signals, including EEG, EOG, EMG, and cardiorespiratory metrics, PSG presents a patient's sleep architecture. However, its dependency on complex equipment and expertise confines its use to specialized clinical settings. Addressing these limitations, our study aims to perform PSG by developing a system that requires only a single EEG measurement. We propose a novel system capable of reconstructing multi-signal PSG from a single-channel EEG based on a masked autoencoder. The masked autoencoder was trained and evaluated using the Sleep-EDF-20 dataset, with mean squared error as the metric for assessing the similarity between original and reconstructed signals. The model demonstrated proficiency in reconstructing multi-signal data. Our results present promise for the development of more accessible and long-term sleep monitoring systems. This suggests the expansion of PSG's applicability, enabling its use beyond the confines of clinics.
@article{arxiv.2311.07868,
title = {Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography},
author = {Young-Seok Kweon and Gi-Hwan Shin and Heon-Gyu Kwak and Ha-Na Jo and Seong-Whan Lee},
journal= {arXiv preprint arXiv:2311.07868},
year = {2023}
}
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Proc. 12th IEEE International Winter Conference on Brain-Computer Interface