用深度学习从脑电图自动筛查儿童阻塞性睡眠呼吸暂停，效果优于传统方法。
Deep Learning Approaches for Sleep Apnea Classification from Polysomnographic EEG Signals
- 对比多种深度模型与信号表示，用拓扑数据分析特征提升识别效果。
- 在2410名患儿训练、575名测试下，最佳模型AUC达0.750。
- 结果揭示年龄、性别、病情严重程度等对模型性能有显著影响。
通过多导睡眠图诊断睡眠呼吸暂停仍需大量资源且依赖耗时的人工分析。近年研究表明，睡眠呼吸暂停事件可在脑电图（EEG）中反映中枢神经系统变化。但多数研究仅使用单一特征类型，在不同数据集上搭配不同分类算法。本文针对儿科患者单个数据集，全面比较深度学习架构与特征表示在多通道EEG上的自动化睡眠呼吸暂停检测能力。评估了视觉变压器（Vision Transformer）与图注意力网络（Graph Attention Network），采用原始时间信号、短时傅里叶变换谱图、基于相干性的图结构及两种拓扑数据分析（TDA）特征。通过年龄与性别匹配的训练测试集，模型在2410名患儿上训练，于575名患儿上测试。基于TDA特征的视觉变压器模型达到最高测试AUC 0.750。分层分析显示，患者年龄、性别、AHI严重程度及睡眠阶段（N1, N2, N3, REM）均导致性能显著差异。结果证明基于EEG的自动化OSA筛查可行，同时凸显临床部署的关键挑战。
原文摘要 · Abstract (English)
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.
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