电子科技2026，Vol.39Issue(1)：73-80,8.DOI:10.16180/j.cnki.issn1007-7820.2026.01.010
基于编码器和注意力机制的睡眠呼吸障碍多分类方法
Research on Multi-Class Classification Method for Sleep-Disordered Breathing Based on Encoder and Attention Mechanism
摘要
Abstract
SA(Sleep Apnea)is a common sleep disorder.Although the traditional PSG(Polysomnography)is the gold standard for diagnosing SA,it is time-consuming and expensive.To address this issue,this study proposes a novel detection method based on ECG(Electrocardiogram)and SpO2(Blood oxygen saturation)signals.The features of ECG and SpO2 are analyzed,and a MSCNN(Multi-Scale Convolutional Neural Network)model combined with an Encoder-SE(Squeeze-and-Excitation)network model is utilized for feature training and classification.MSCNN enhances the analysis effect of the ana-lyzed signals by obtaining ECG and SpO2 feature quantities of different time lengths.The Encoder-SE network further im-proves the feature representation ability.Through the SE module,it adaptively adjusts the importance of features to ensure that the model focuses on key features.The experimental results show that the proposed method has an average accuracy of 93.29％,providing new ideas and effective tools for the clinical diagnosis and treatment of SA.关键词
睡眠呼吸暂停综合征/深度学习/心电图/外周血氧饱和度/多模态卷积神经网络/特征融合/编码器/注意力机制Key words
sleep apnea syndrome/deep learning/electrocardiogram/peripheral blood oxygen saturation/multimodal convolutional neural network/feature fusion/encoder/attention mechanism分类
信息技术与安全科学引用本文复制引用
楼利军,何晓玉,蒋明峰..基于编码器和注意力机制的睡眠呼吸障碍多分类方法[J].电子科技,2026,39(1):73-80,8.基金项目
国家重点研发计划(2023YFE0205600)National Key R&D Program of China(2023YFE0205600) （2023YFE0205600）
