基于类重平衡无监督域自适应的睡眠分期算法

Sleep Staging Algorithm Based on Class Rebalancing Unsupervised Domain Adaptation

睡眠障碍严重影响人体健康,基于脑电图的深度学习自动睡眠分期算法可帮助专家正确诊断患者的睡眠障碍。然而,训练数据的不平衡不利于少数类特征的学习,且由于数据分布的差异,训练数据上得到的自动睡眠分期模型在实际数据上的准确性往往会下降。对此,提出了一种融合类重平衡策略和半监督学习的无监督域自适应算法,并引入均衡损失函数以缓解睡眠分期数据集中数据不平衡的问题;同时,设计了平均教师方法,并引入随机的输出插值和相关置信度阈值以提升伪标签的精确度,通过鉴别器网络优化目标域数据的特征分布,从而改善模型在目标域上的分类准确性。在SHHS,Sleep-EDF和ISRUC-Sleep数据集上进行实验,证明所提算法的有效性,相比直接迁移算法的准确率提高了3.28%~13.27%,相比域统计对齐算法的准确率提高了6.73%~14.52%,相比自适应域统计对齐算法的准确率提高了0.78%~5.82%。

Sleep disorders seriously affect human health.Deep learning automatic sleep staging algorithms based on electroencephalogram can assist experts in accurately diagnosing patients'sleep disorders.However,the imbalance in training data hinders the learning of minority class features,and due to the differences in data distribution,the accuracy of automatic sleep staging models trained on training data often decreases when applied to real-world data.To address this issue,an unsupervised domain adaptation algorithm that combines class re-balancing strategies and semi-supervised learning is proposed.A balanced loss function is introduced to mitigate data imbalance issue in sleep staging datasets.Additionally,an average teacher method is designed,and random output interpolation and related confidence thresholding are introduced to improve the accuracy of pseudo-labels.The feature distribution of target domain data is optimized through a discriminator network,thereby improving the classification accuracy on the target domain.Experiments conducted on the SHHS,Sleep-EDF,and ISRUC-Sleep datasets demonstrate the effectiveness of the proposed algorithm.Compared to direct transfer,the accuracy improves by 3.28%to 13.27%.Compared to domain statistical alignment,the accuracy is increased by 6.73%to 14.52%.Compared to adaptive domain statistical alignment methods,the accuracy is increased by 0.78%to 5.82%.