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
11/14/2023
cs.LGcs.AIeess.SP

Official code: facebookresearch/AudioMAE (672 stars, last commit 2024-04-05).

Abstract

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.

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Code Implementations(1)

facebookresearch/AudioMAEOfficial60%

This repo hosts the code and models of "Masked Autoencoders that Listen".

67257Shell, PythonJul 8, 20222 years agoNOASSERTION

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Cite this paper

@article{kweon2023multisignal,
  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},
  year   = {2023},
  eprint = {2311.07868},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.07868v1}
}

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