Single-channel EEG based insomnia detection with domain adaptation. (December 2021) - Qu, Wei; Kao, Chien-Hui; Hong, Hong; Chi, Zheru; Grunstein, Ron; Gordon, Christopher; Wang, Zhiyong
Abstract: 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. Highlights: Domain adaptation can properly address data scarcity issue in insomnia detection. Extracting common sleep related features by training a sleep stage classifier is crucial in modelling. Preventing data acquisition related private features from impacting model performance is an indispensable part of the model. Feeding whole overnight EEG raw signals can help model better learn temporal context. The proposed approach can transfer models trained on large sleep dataset to other sleep related tasks with few data available.