Unsupervised Domain Adaptation Algorithm Improved with Balanced Sampling

作者：Hengyi Shao, Lei Li, Lin Zhang · 年份：2023 · DOI：10.1109/ic-nidc59918.2023.10390657 · 研究领域：EEG and Brain-Computer Interfaces、Sleep and Work-Related Fatigue、Obstructive Sleep Apnea Research

Sleep disorders seriously affect human health. Leveraging deep learning methods and Electroencephalography, automatic sleep staging can aid experts in accurately diagnosing patients' sleep disorders. However, the imbalance of the training data undermines the learning of minority class features. Besides, the performance of the automatic sleep staging model obtained on the training data tends to decrease on the practical data due to the difference in data distribution. As a result, an unsupervised domain adaptation algorithm combined with class rebalancing strategy and semi-supervised learning is proposed to solve the above problems. To alleviate data imbalance in sleep staging datasets, our paper devises a balanced sampler. Random logit interpolation and relative confidence threshold are introduced to improve the accuracy of pseudo-labels. Moreover, distribution alignment is introduced to mitigate the dissimilarity in data distribution between the source and target domains. Through experiments, the effectiveness of the proposed method is proved on the SHHS, Sleep-EDF and ISRUC-Sleep datasets. The average improvement in accuracy is around 5.27%. Both the F1 score and recall rate have also been significantly improved.