AI 中文总结
研究基于儿科受试者单数据集多通道EEG，比较深度学习架构和特征表示自动检测睡眠呼吸暂停，评估视觉Transformer和图注意力网络在不同信号表示上的性能，通过年龄和性别匹配训练与测试，基于TDA特征的视觉Transformer模型取得最佳AUC，揭示性能差异及临床挑战。
AI 中文摘要
通过多导睡眠图进行睡眠呼吸暂停诊断仍然资源密集，且依赖耗时的手动数据分析和评分。近期研究表明可通过脑电图（EEG）信号检测睡眠呼吸暂停事件的中枢神经系统影响。然而，大多数工作在不同数据集上使用单一特征类型并结合不同分类算法。本文对基于深度学习架构和特征表示从儿科受试者单数据集多通道EEG中自动检测睡眠呼吸暂停进行全面比较。评估了视觉Transformer和图注意力网络在不同信号表示上的性能，包括原始时间信号、短时傅里叶变换频谱图、基于相干性的图以及两种拓扑数据分析（TDA）衍生特征。通过年龄和性别匹配训练集和测试集，在2410名儿科受试者上训练并在575名儿科受试者上测试。基于TDA特征训练的视觉Transformer模型取得了最佳测试AUC为0.750。对患者人口统计学（年龄、性别、AHI严重程度）和睡眠阶段（N1、N2、N3、REM）的分层分析揭示了显著的性能差异。结果证明了基于EEG的自动OSA筛查的可行性，同时突出了临床部署的关键挑战。
英文摘要
Sleep apnea diagnosis via polysomnography remains resource intensive and relies on time consuming manual data analysis and scoring. Recent work has demonstrated that central nervous system effects of sleep apnea events can be detected through electroencephalogram (EEG) signals. However, most work uses a single feature type on various datasets combined with different classification algorithms. In this work, we present a comprehensive comparison of deep learning architectures and feature representations for automated sleep apnea detection from multichannel EEG on a single dataset of pediatric subjects. We evaluate Vision Transformers and Graph Attention Networks across distinct signal representations: raw temporal signals, short-time Fourier transform spectrograms, coherence based graphs, and two topological data analysis (TDA) derived features. Using age and sex matching of our train and test sets, we train on 2410 pediatric subjects and test on 575 pediatric subjects. We achieve a best test AUC of 0.750 using a vision transformer based model trained on TDA features. Stratified analysis across patient demographics (age, sex, AHI severity) and sleep stages (N1, N2, N3, REM) reveals significant performance variation. Our results demonstrate the feasibility of EEG based automated OSA screening while highlighting essential challenges for clinical deployment.
CommentsIEEE EMBC 2026
