Work

Severity Prediction of Obstructive Sleep Apnea Using Transformed 2D Oxygen Saturation Signals

Year: 2025

Type: article

Abstract: Keywords: obstructive sleep apnea (OSA), apnea-hypopnea index (AHI), oxygen saturation (SpO 2 ), deep learning In this paper, we present a deep-learning-based method for predicting obstructive sleep apnea (OSA) severity using 2D oxygen saturation (SpO 2 ) signal representations.Our previous works have demonstrated that unsegmented overnight 1D SpO 2 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 SpO 2 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.

Source: Sensors and Materials

DOI: 10.18494/sam5765

Authors: Yi-Cheng Wu, Cheng-Yu Yeh, Chun-Cheng Lin

Institution: National Chin-Yi University of Technology

Language: English

Topic: Obstructive Sleep Apnea Research

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $748