Young Researcher Paper Award 2025
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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 37, Number 12(2) (2025)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 5535-5540
S&M4261 Letter https://doi.org/10.18494/SAM5765 Published: December 19, 2025 Severity Prediction of Obstructive Sleep Apnea Using Transformed 2D Oxygen Saturation Signals [PDF] Yi-Cheng Wu, Cheng-Yu Yeh, and Chun-Cheng Lin (Received June 2, 2025; Accepted December 1, 2025) Keywords: obstructive sleep apnea (OSA), apnea-hypopnea index (AHI), oxygen saturation (SpO2), deep learning
In this paper, we present a deep-learning-based method for predicting obstructive sleep apnea (OSA) severity using 2D oxygen saturation (SpO2) signal representations. Our previous works have demonstrated that unsegmented overnight 1D SpO2 signals can achieve high accuracy in predicting the apnea-hypopnea index and classifying OSA severity. Extending this approach, we introduce a novel transformation of the original 1D SpO2 signal into a 2D representation, enabling it to be processed similarly to an image signal and then trained by deep neural networks. This signal transformation is designed to facilitate subsequent studies into model interpretability. Experimental results show that the proposed approach reaches competitive performance compared with state-of-the-art baselines.
Corresponding author: Cheng-Yu Yeh  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yi-Cheng Wu, Cheng-Yu Yeh, and Chun-Cheng Lin, Severity Prediction of Obstructive Sleep Apnea Using Transformed 2D Oxygen Saturation Signals, Sens. Mater., Vol. 37, No. 12, 2025, p. 5535-5540. |
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