用动态加权融合多模态数据，提升睡眠分期准确率
SleepGMUformer: A gated multimodal temporal neural network for sleep staging
- 设计门控机制实时分配不同传感器数据权重
- 在两个数据集上准确率分别达85.03%和94.54%
- 适合睡眠监测与智能医疗系统开发人员
睡眠分期是评估睡眠质量与诊断睡眠障碍的关键方法。现有深度学习方法存在两大挑战：1）后融合技术忽略不同模态的贡献差异；2）未经处理的睡眠数据会干扰频域信息。为此，本文提出一种门控多模态时序神经网络，用于处理包含心率、运动、步数、脑电（Fpz-Cz, Pz-Oz）和眼电的多域睡眠数据，数据来源为WristHR-Motion-Sleep与SleepEDF-78。模型包含：1）预处理模块，实现特征对齐、缺失值处理与脑电去趋势；2）时域特征提取模块，捕捉复杂睡眠特征；3）动态融合模块，实现模态权重实时调整。实验表明，该模型在SleepEDF-78数据集上分类准确率达85.03%，在WristHR-Motion-Sleep数据集上达94.54%，优于当前最优模型1.00%-4.00%。
原文摘要 · Abstract (English)
Sleep staging is a key method for assessing sleep quality and diagnosing sleep disorders. However, current deep learning methods face challenges: 1) postfusion techniques ignore the varying contributions of different modalities; 2) unprocessed sleep data can interfere with frequency-domain information. To tackle these issues, this paper proposes a gated multimodal temporal neural network for multidomain sleep data, including heart rate, motion, steps, EEG (Fpz-Cz, Pz-Oz), and EOG from WristHR-Motion-Sleep and SleepEDF-78. The model integrates: 1) a pre-processing module for feature alignment, missing value handling, and EEG de-trending; 2) a feature extraction module for complex sleep features in the time dimension; and 3) a dynamic fusion module for real-time modality weighting.Experiments show classification accuracies of 85.03% on SleepEDF-78 and 94.54% on WristHR-Motion-Sleep datasets. The model handles heterogeneous datasets and outperforms state-of-the-art models by 1.00%-4.00%.
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