通过建模弱标签集推进睡眠检测：一种新颖的弱监督学习方法
Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach
AI总结：
本文提出一种弱监督睡眠检测方法，将弱标签数量建模为二项分布，并证明最大化似然等价于最小化软交叉熵损失，在MESA数据集上训练的LSTM在准确率和校准方面优于传统算法。
摘要：
理解睡眠和活动模式对身心健康至关重要。本研究提出了一种在无法获得可靠真实标签的场景下使用弱监督学习进行睡眠检测的新方法。所提方法依赖一组弱标签，这些弱标签来源于传统睡眠检测算法生成的预测结果。我们提出了一种新颖的广义非线性统计模型，将弱睡眠标签的数量建模为二项分布的观测结果。二项分布中的睡眠概率与基于体动记录仪训练用于检测睡眠的神经网络的输出相关联。我们证明，最大化该模型的似然函数等价于最小化软交叉熵损失。此外，我们探索了将Brier分数用作弱标签损失函数。所提出的建模框架的有效性在Multi-Ethnic Study of Atherosclerosis数据集上得到了验证。在软交叉熵上训练的LSTM在准确率和模型校准方面优于传统睡眠检测算法、其他神经网络架构和损失函数。本研究不仅在真实数据稀缺的场景下推进了睡眠检测技术，还通过引入建模弱标签集的创新方法，为更广泛的弱监督学习领域做出了贡献。
英文摘要：
Understanding sleep and activity patterns plays a crucial role in physical and mental health. This study introduces a novel approach for sleep detection using weakly supervised learning for scenarios where reliable ground truth labels are unavailable. The proposed method relies on a set of weak labels, derived from the predictions generated by conventional sleep detection algorithms. Introducing a novel approach, we suggest a novel generalised non-linear statistical model in which the number of weak sleep labels is modelled as outcome of a binomial distribution. The probability of sleep in the binomial distribution is linked to the outcomes of neural networks trained to detect sleep based on actigraphy. We show that maximizing the likelihood function of the model, is equivalent to minimizing the soft cross-entropy loss. Additionally, we explored the use of the Brier score as a loss function for weak labels. The efficacy of the suggested modelling framework was demonstrated using the Multi-Ethnic Study of Atherosclerosis dataset. A \gls{lstm} trained on the soft cross-entropy outperformed conventional sleep detection algorithms, other neural network architectures and loss functions in accuracy and model calibration. This research not only advances sleep detection techniques in scenarios where ground truth data is scarce but also contributes to the broader field of weakly supervised learning by introducing innovative approach in modelling sets of weak labels.
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