Multi-Signal Reconstruction Using Masked Autoencoder From EEG During
  Polysomnography
Young-Seok Kweon Gi-Hwan Shin Heon-Gyu Kwak Ha-Na Jo Seong-Whan Lee
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.