Optimizing EEG-Based Sleep Staging: Adversarial Deep Learning Joint Domain Adaptation

Abstract

Sleep-stage classification is a critical aspect of understanding sleep patterns in sleep research and healthcare. However, challenges arise when dealing with a limited number of labeled samples in the target domain. Traditional methods in Deep Learning (DL) and Domain Adaptation (DA) globally compare feature distributions, often overlooking intricate decision boundaries between sleep-stage classes. This results in ambiguous features near class boundaries, diminishing classification accuracy. The conventional two-step process of using a pre-trained classifier for predictions and assessing uncertainty fails to effectively incorporate unlabeled data in classifier training, neglecting the complexities of the target domain. To address these challenges, we propose Adversarial Deep Learning Joint Domain Adaptation (ADLJDA). This innovative approach integrates an adversarial model and deploys two distinct sleep-stage classifiers as discriminators, allowing for a nuanced consideration of class boundaries during feature distribution alignment. ADLJDA also incorporates an entropy measure with cross-entropy loss during training to harness information from unlabeled data in the target domain. Experimental results on three benchmark EEG datasets highlight the efficacy of ADLJDA. The approach consistently demonstrates the ability to generate robust and transferable features, mitigating the impact of ambiguous features near original class boundaries. Importantly, ADLJDA shows a significant improvement in classification accuracy compared to existing state-of-the-art DA methods, even in datasets with intricate patterns and complexities. This research contributes to advancing sleep-stage classification methodologies, offering a promising solution for enhanced accuracy in real-world applications and furthering our understanding of sleep-related phenomena.

Keywords

• Adversarial learning
• channel optimization
• classification accuracy
• deep learning
• domain adaptation
• EEG signal