Deep learning based sleep stage classification

PhD by Publication


Ji, Xiaopeng. 2023. Deep learning based sleep stage classification. PhD by Publication Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/z4y9q
Title

Deep learning based sleep stage classification

TypePhD by Publication
AuthorsJi, Xiaopeng
Supervisor
1. FirstProf Yan Li
2. SecondProf Paul Wen
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages111
Year2023
PublisherUniversity of Southern Queensland
Place of PublicationAustralia
Digital Object Identifier (DOI)https://doi.org/10.26192/z4y9q
Abstract

Sleep plays an essential role in humans’ life and sleep stage classification is the first step for sleep research and sleep disorder diagnosis. This research aims to classify sleep stages automatically using deep learning methods, which can help clinicians identify sleep problems. Polysomnograms (PSGs), including electroencephalography (EEG), electromyogram (EMG), electrocardiogram (ECG), and electrooculogram (EOG), are signals collected through placing electrodes on the scalp cross different locations, which are powerful tools for sleep stages classification and sleep disorders identification. To classify sleep stages more effectively and efficiently, three models were developed in this research, namely the jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) model, the 3DSleepNet model, and the MixSleepNet model. For all the three models, key features are extracted from multi-channel signals to aggregate spatial and temporal information. For the JK-STGCN model, the connections among different bio-signal channels from the identical epochs and their neighbouring epochs can be obtained through two adaptive adjacency matrices learning methods. A jumping knowledge spatial-temporal graph convolution module helps this model to extract spatial features from the graph convolutions efficiently and temporal features are extracted from its common standard convolutions to learn the transition rules among sleep stages. For the 3DSleepNet model, the intrinsic connections among different bio-signals and different frequency bands in time series and time-frequency are learned by the 3D convolutional layers, while the frequency relations are learned by the 2D convolutional layers. The partial dot-product attention layers help this model find the most important channels and frequency bands in different sleep stages. A long short-term memory unit is added to learn the transition rules among neighbouring epochs. For the MixSleepNet model, the 3D convolution branch can explore the correlations between multi-channel signals and multi-band waves in each channel in the time series, while the graph convolution branch can explore the connections between each channel and each frequency band. The experiments on ISRUC-S3 and ISRUC-S1 demonstrate that the JK-STGCN model outperforms the 1D-CNNs, 2D-CNNs, U2-Net, and other GCN models on these two subsets, while the 3D-CNN model achieves similar performances with a faster speed, and the MixSleepNet model achieves better performances based on the second expert’s label with a notable improvement of sleep stage 1 precision.

KeywordsGCN; CNN; Sleep stage classification
Related Output
Has partJumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification
Has part3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning
Has partMixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461103. Deep learning
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author/creator.

Byline AffiliationsSchool of Mathematics, Physics and Computing
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Related outputs

An automatic method using MFCC features for sleep stage classification
Pei, Wei, Li, Yan, Wen, PPeng, Yang, Fuwen and Ji, Xiaopeng. 2024. "An automatic method using MFCC features for sleep stage classification." Brain Informatics. 11. https://doi.org/10.1186/s40708-024-00219-w
MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model
Ji, Xiaopeng, Li, Yan, Wen, Peng, Barua, Prabal and Acharya, U Rajendra. 2024. "MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model ." Computer Methods and Programs in Biomedicine. 244. https://doi.org/10.1016/j.cmpb.2023.107992
3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning
Ji, Xiaopeng, Li, Yan and Wen, Peng. 2023. "3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning." IEEE Transactions on Neural Systems and Rehabilitation Engineering. 31, pp. 3513-3523. https://doi.org/10.1109/TNSRE.2023.3309542
Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification
Ji, Xiaopeng, Li, Yan and Wen, Peng. 2022. "Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification." IEEE Transactions on Neural Systems and Rehabilitation Engineering. 30, pp. 1464-1472. https://doi.org/10.1109/TNSRE.2022.3176004