用注意力机制提升可穿戴睡眠脑电伪迹检测与定位精度
Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms
- 基于卷积神经网络与注意力模块，自动识别脑电信号中的伪迹
- 在4%含伪迹的片段中实现0.88的曲线下面积与0.81的检出率
- 适合睡眠研究中移动式单通道脑电数据的自动化分析
当前睡眠脑电伪迹检测方法从阈值法到机器学习不等，但在单通道可穿戴脑电中的应用仍有限。本文提出一种融合卷积块注意力模块（CNN-CBAM）的卷积神经网络模型，利用注意力图实现伪迹检测与定位。模型在72段由18名健康参与者（平均年龄68.05岁，标准差±5.02）在家监测获得的标注脑电数据上训练/调优，并在26段来自6名健康参与者（平均年龄68.33岁，标准差±4.08）的独立数据上测试，其中4%的脑电时段含伪迹。CNN-CBAM在所有对比方法中表现最优，达到0.88的受试者工作特征曲线下面积（AUC）、0.81的敏感性与0.86的特异性。当注意力阈值设定为0.66时，注意力图在被识别为伪迹的时段内实现0.61的敏感性与0.63的特异性。本研究证明了在可穿戴睡眠脑电中自动化伪迹检测与定位的可行性。
原文摘要 · Abstract (English)
Current methods for detecting artifacts in sleep EEG range from threshold-based algorithms to machine learning approaches, yet applications remain limited for single-channel mobile EEG. We propose a convolutional neural network (CNN) model incorporating a convolutional block attention module (CNN-CBAM) to detect and localize artifacts in sleep EEG using attention maps. We benchmarked this model against 6 other machine learning and signal processing approaches. We trained/tuned all models on 72 manually annotated EEG recordings obtained during home-based monitoring from 18 healthy participants with a mean (SD) age of 68.05 y ($\pm$5.02). We tested them on 26 separate recordings from 6 healthy participants with a mean (SD) age of 68.33 y ($\pm$4.08), which contained artifacts in 4\% of epochs. CNN-CBAM achieved the highest area under the receiver operating characteristic curve (0.88), sensitivity (0.81), and specificity (0.86) among the tested approaches. Under the ideal choice of an attention threshold of 0.66, the attention maps from CNN-CBAM localized artifacts within detected artifact epochs with a sensitivity of 0.61 and specificity of 0.63. This work demonstrates the feasibility of automating artifact detection and localization in wearable sleep EEG.
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