IJCAI Conference 2024 Conference Paper
ATTA: Adaptive Test-Time Adaptation for Multi-Modal Sleep Stage Classification
- Ziyu Jia
- Xihao Yang
- Chenyang Zhou
- Haoyang Deng
- Tianzi Jiang
Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. Although some recent studies have made great strides in sleep stage classification performance, direct application to multi-modal sleep data with cross-domain distributional variations still poses challenges: 1) How to retain the sleep knowledge acquired by the model from the source domain during cross-domain adaptation to avoid catastrophic forgetting. 2) How to evaluate the contribution of different modalities in identifying specific sleep stages to serve test-time adaptation (TTA). 3) How to dynamically adapt the sleep model to different distribution shift in data domains of different subjects. To address these challenges, we propose an Adaptive Test-Time Adaptation (ATTA) method, a multi-modal test-time adaptation method for sleep stage classification. Specifically, the intra-modal retained-adaptive module is proposed for adapting to the target domain data while retaining the sleep knowledge acquired from the source domain to avoid catastrophic forgetting. The inter-modal contribution assessment module is designed to adaptively assess the contribution of each modality to the identification of specific sleep stages. Furthermore, the adaptive learning rate strategy utilizes a memory bank to record data from different subjects during testing, and based on this, it measures the differences between the target subject and those in the memory bank. According to the difference, the model adapts to the subject samples with different learning rates. We conduct experiments on mutual migration on two sleep datasets, SleepEDF and SHHS. The results show that our ATTA method outperforms state-of-the-art baselines in sleep stage classification.