  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.
