School of Electronic Engineering and Computer Science, Queen Mary, University of London, United Kingdom.
Physiol Meas. 2020 Jul 1;41(6):064004. doi: 10.1088/1361-6579/ab921e.
Brain waves vary between people. This work aims to improve automatic sleep staging for longitudinal sleep monitoring via personalization of algorithms based on individual characteristics extracted from sleep data recorded during the first night.
As data from a single night are very small, thereby making model training difficult, we propose a Kullback-Leibler (KL) divergence regularized transfer learning approach to address this problem. We employ the pretrained SeqSleepNet (i.e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model. This is done by adding the KL divergence between the output of the subject independent model and it of the personalized model to the loss function during finetuning. In effect, KL-divergence regularization prevents the personalized model from overfitting to the single-night data and straying too far away from the subject independent model.
Experimental results on the Sleep-EDF Expanded database consisting of 75 subjects show that sleep staging personalization with single-night data is possible with help of the proposed KL-divergence regularization. On average, we achieve a personalized sleep staging accuracy of 79.6%, a Cohen's kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of 71.8%, and a specificity of 94.2%.
We find both that the approach is robust against overfitting and that it improves the accuracy by 4.5 percentage points compared to the baseline method without personalization and 2.2 percentage points compared to it with personalization but without regularization.
脑电波因人而异。本工作旨在通过基于从第一晚记录的睡眠数据中提取的个体特征对算法进行个性化，从而改善纵向睡眠监测的自动睡眠分期。
由于单晚数据非常小，从而使模型训练变得困难，因此我们提出了一种基于 Kullback-Leibler（KL）散度正则化的迁移学习方法来解决这个问题。我们将预训练的 SeqSleepNet（即独立于主题的模型）作为起点，并使用单晚个性化数据对其进行微调，以获得个性化模型。这是通过在微调过程中将 KL 散度（独立于主题的模型和个性化模型的输出之间的 KL 散度）添加到损失函数中完成的。实际上，KL 散度正则化可防止个性化模型过度拟合单晚数据并远离独立于主题的模型。
在由 75 个受试者组成的 Sleep-EDF Expanded 数据库上进行的实验结果表明，借助于所提出的 KL 散度正则化，可以使用单晚数据进行睡眠分期个性化。平均而言，我们实现了个性化睡眠分期的准确性为 79.6%，Cohen's kappa 为 0.706，宏观 F1 得分为 73.0%，敏感性为 71.8%，特异性为 94.2%。
我们发现该方法不仅具有鲁棒性，不易过度拟合，而且与没有个性化的基线方法相比，准确性提高了 4.5 个百分点，与具有个性化但没有正则化的方法相比，准确性提高了 2.2 个百分点。
