基于U-Net与SAM模块的睡眠呼吸暂停检测分析
Analysis of Sleep Apnea Detection Based on U-Net and SAM Module
谢仕宇 1杨其宇1
作者信息
摘要
阐述一种基于脉搏血氧饱和度(SpO2)的U-Net模型,通过编码一解码结构提取血氧信号的多层次特征.使用SAM模块,自适应地提高氧减事件的权重.在SleepApnea-ECG数据集进行交叉验证,平均准确性、灵敏度、特异性分别为95.69％、95.52％、95.59％,与其他模型对比有更高的检测精度.
Abstract
This paper describes a U-Net model based on pulse oxygen saturation(SpO2),which extracts multi-level features of blood oxygen signals through an encoding decoding structure.It uses the SAM module to adaptively increase the weight of oxygen reduction events.Cross validation was performed on the SleepApnea ECG dataset,with an average accuracy,sensitivity,and specificity of 95.69％,95.52％,and 95.59％,respectively,indicating higher detection accuracy compared to other models.
关键词
检测技术/U-Net/脉搏血氧饱和度/SAM模块Key words
detection technology/U-Net/pulse oxygen saturation/SAM module引用本文复制引用
出版年
2024
NETL
NSTL
万方数据