School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia.
Woolcock Institute of Medical Research, The University of Sydney, Sydney, New South Wales, Australia.
Comput Biol Med. 2021 Dec;139:104989. doi: 10.1016/j.compbiomed.2021.104989. Epub 2021 Oct 27.
Insomnia is one of the most common sleep disorders which can dramatically impair life quality and negatively affect an individual's physical and mental health. Recently, various deep learning based methods have been proposed for automatic and objective insomnia detection, owing to the great success of deep learning techniques. However, due to the scarcity of public insomnia data, a deep learning model trained on a dataset with a small number of insomnia subjects may compromise the generalization capacity of the model and eventually limit the performance of insomnia detection. Meanwhile, there have been a number of public EEG datasets collected from a large number of healthy subjects for various sleep research tasks such as sleep staging. Therefore, to utilize such abundant EEG datasets for addressing the data scarcity issue in insomnia detection, in this paper we propose a domain adaptation based model to better extract insomnia related features of the target domain by leveraging stage annotations from the source domain. For each domain, two pairs of common encoder and private encoder are firstly trained to extract sleep related features and sleep irrelevant features, respectively. In order to further discriminate source domain and target domain, a domain classifier is introduced. Then, the common encoder of the target domain will be used together with the Long Short Term Memory (LSTM) network for insomnia detection. To the best of our knowledge, this is the first deep learning based domain adaptation model using single channel raw EEG signals to detect insomnia at subject level. We use the Montreal Archive of Sleep Studies (MASS) dataset which contains only healthy subjects as source domain and two datasets which contain both healthy and insomnia subjects as target domain to validate our model's generalizability. Experimental results on the two target domain datasets (a public one and an in-house one) demonstrate that our model generalizes well on two target domain datasets with different sampling rates. In particular, our proposed method is able to improve insomnia detection performance from 50.0% to 90.9% and 66.7%-79.2% in terms of accuracy on the two target domain datasets, respectively.
失眠是最常见的睡眠障碍之一，它会极大地降低生活质量，并对个人的身心健康产生负面影响。最近，由于深度学习技术的巨大成功，各种基于深度学习的方法被提出用于自动和客观的失眠检测。然而，由于失眠数据的稀缺，在一个只有少数失眠患者的数据集上训练的深度学习模型可能会影响模型的泛化能力，并最终限制失眠检测的性能。同时，已经有许多公共 EEG 数据集是从大量健康受试者中收集的，用于各种睡眠研究任务，如睡眠分期。因此，为了利用这些丰富的 EEG 数据集来解决失眠检测中的数据稀缺问题，在本文中，我们提出了一种基于领域自适应的模型，通过利用源域的分期注释来更好地提取目标域的失眠相关特征。对于每个域，首先训练两对公共编码器和私有编码器，分别提取与睡眠相关的特征和与睡眠无关的特征。为了进一步区分源域和目标域，引入了一个域分类器。然后，将目标域的公共编码器与长短期记忆 (LSTM) 网络一起用于失眠检测。据我们所知，这是第一个使用单通道原始 EEG 信号的基于深度学习的领域自适应模型，用于在个体水平上检测失眠。我们使用只包含健康受试者的蒙特利尔睡眠研究档案 (MASS) 数据集作为源域，以及包含健康和失眠受试者的两个数据集作为目标域来验证我们模型的泛化能力。在两个目标域数据集（一个公共数据集和一个内部数据集）上的实验结果表明，我们的模型在具有不同采样率的两个目标域数据集上具有良好的泛化能力。特别是，我们提出的方法能够将两个目标域数据集的失眠检测性能分别从 50.0%提高到 90.9%和 66.7%-79.2%。
