Work

SASBLS: An Advanced Model for Sleep Apnea Detection Based on Single-Channel SpO2

Year: 2025

Type: article

Abstract: (1) Background: Sleep Apnea Syndrome (SAS) poses a serious threat to human health. Existing SpO2-based automatic SAS detection models have a relatively low accuracy in detecting positive samples because they overlook the global information from the Apnea-Hypopnea Index (AHI). (2) Methods: To address this problem, we proposed a multi-task model for SAS detection and AHI prediction based on single-channel SpO2. Benefiting from the characteristics of the Broad Learning System (BLS), this model optimizes itself by leveraging the differences between all-night SpO2 information and sample SpO2 information, enabling the two tasks to promote each other. (3) Results: The model was verified using 7906 all-night SpO2 data from the publicly available Sleep Heart Health Study (SHHS) dataset, and the SAS detection performance has reached the state-of-the-art level. In addition, the performance of samples with different lengths in the two tasks was also explored. (4) Conclusions: The model we proposed can balance and effectively perform both SAS detection and AHI prediction simultaneously.

Source: Sensors

DOI: 10.3390/s25051523

Authors: Yichong She, Di Zhang, Jinbo Sun, Xuejuan Yang, Xiao Zeng, Wei Qin

Institutions: Xidian University, Guangzhou Institute 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: $3,137