Annual Conference of KIPS (한국정보처리학회:학술대회논문집)

  • 2024.10a
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  • Pages.539-542
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  • 2024
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  • 2005-0011(pISSN)
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  • 2671-7298(eISSN)
Korea Information Processing Society (한국정보처리학회)
권호 표지

Multivariate Masked Autoencoders for Obstructive Sleep Apnea Diagnosis

  • Thanh-Cong Do (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Hyung-Jeong Yang (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Hyeong-Chae Yang (Otolaryngology Department, Chonnam National University)
  • Published : 2024.10.31
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Abstract

Obstructive sleep apnea (OSA) is a common sleep disorder, causing disrupted sleep and reduced oxygen levels in the blood. Full-night polysomnography (PSG) and home sleep apnea tests (HSAT) may offer acceptable diagnosis results but have shown some limitations. In recent years, deep learning and data analysis methods are progressively employed on electronic health records, and various methods have been developed for OSA event detection. Self-supervised learning has shown some advantages over supervised training methods, by learning more generalized feature representation of data. Unlike text or image processing, the high information density in multivariate time-series data makes it more challenging to utilize self-supervised learning. In this research, based on the characteristics of sleep dataset, we propose a self-supervised approach with Masked Autoencoder, which masks portions of the input and attempting to reconstruct them. This enables the model to learn more generalized features from unlabeled data. We evaluate our proposed framework with three independent sleep datasets, which have shown significant improvement compared to supervised learning models.

Keywords

Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korea government(MSIT) This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (RS-2023-00208397) This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2024-RS-2024-00437718) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation)

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