Recently, deep learning for automated sleep stage classification has been introduced with promising results. However, as many challenges impede their routine application, automatic sleep scoring algorithms are not widely used. Typically, polysomnography (PSG) uses multiple channels for higher accuracy; however, the disadvantages include a requirement for a patient to stay one or more nights in the lab wearing uncomfortable sensors and wires. To avoid the inconvenience caused by the multiple channels, we aimed to develop a deep learning model for use in clinical decision support systems (CDSSs) and combined convolutional neural networks and a transformer for the supervised learning of three classes of sleep stages only with single-channel EEG data (C4-M1). The data for training, validation, and test were derived from 1590, 341, and 343 polysomnography recordings, respectively. The developed model yielded an overall accuracy of 91.4%, comparable with that of human experts. Based on the severity of obstructive sleep apnea, the model’s accuracy was 94.3%, 91.9%, 91.9%, and 90.6% in normal, mild, moderate, and severe cases, respectively. Our deep learning model enables accurate and rapid delineation of three-class sleep staging and could be useful as a CDSS for application in real-world clinical practice.
Recent deep learning studies for sleep stage classification with polysomnography (PSG) data show two directions, either using 1-dimensional (1-D) raw PSG data or spectrogram images time-frequency domain. We propose a novel approach using images generated from time-signal display of a PSG dataset for 5 class sleep stage classification. The motivation of our approach is not only to imitate the way used by human sleep-scoring experts but also to make use of various methods developed in image classification in Deep Learning, such as augmentation techniques, EfficientNet and LSTM. In addition an explainable AI technique such as Class Activation Map (CAM) can be employed for interpreting how a model makes a decision. We, also, work on ''inconsistency'' problems occurring among multiple institutions/hospitals where different capturing sensors are used and the labelling mismatch by human experts in different organizations. To solve the problem, we experiment three different approaches in the network design with data of two institutes and 5 sleep stage classification; (i) 5 class classification, (ii) 10-class classification and then post-processing to 5 classes (iii) 10-to-5 class classification. The 10-to-5 class classification is a network where information of two institutes are embedded inside the network. When information of multi institution is inside the network, the results show higher performance. Our experimental results show that all of three proposed methods based on time-signal images achieves higher accuracy performance compared to state-of-the-art models. INDEX TERMSDeep learning, image classification, sleep, rapid eye movement sleep. DONGYOUNG KIM received the B.E. and M.E. degrees in computer engineering from Hallym University, in 2019 and 2021, respectively, where he is currently pursuing the Ph.D. degree with the Department of Computer Engineering. His research interests include deep learning for medical applications and deep learning based domain adaption for multi-institutional dataset. JAEMIN JEONG received the B.E. and M.E. degrees in computer engineering from Hallym University, in 2019 and 2021, respectively, where he is currently pursuing the Ph.D. degree with the Department of Computer Engineering. He is also a Software Engineer with Mirroroid, where he develops deep learning models for hair and head segmentation. His research interests include deep learning based vision (GAN, segmentation, and object detection) and deep learning model compression for edge computing.
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