Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification
A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electroc...
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| Veröffentlicht in: | IEEE transactions on neural systems and rehabilitation engineering Jg. 30; S. 1464 - 1472 |
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| Sprache: | Englisch |
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IEEE
01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) are utilized to classify sleep stages, after extracting features by a standard convolutional neural network (CNN) named FeatureNet. Intrinsic connections among different bio-signal channels from the identical epoch and neighboring epochs can be obtained through two adaptive adjacency matrices learning methods. A jumping knowledge spatial-temporal graph convolution module helps the JK-STGCN 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. Experimental results on the ISRUC-S3 dataset showed that the overall accuracy achieved 0.831 and the F1-score and Cohen kappa reached 0.814 and 0.782, respectively, which are the competitive classification performance with the state-of-the-art baselines. Further experiments on the ISRUC-S3 dataset are also conducted to evaluate the execution efficiency of the JK-STGCN model. The training time on 10 subjects is 2621s and the testing time on 50 subjects is 6.8s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and U-Net architecture algorithms. Experimental results on the ISRUC-S1 dataset also demonstrate its generality, whose accuracy, F1-score, and Cohen kappa achieve 0.820, 0.798, and 0.767 respectively. |
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| AbstractList | A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) are utilized to classify sleep stages, after extracting features by a standard convolutional neural network (CNN) named FeatureNet. Intrinsic connections among different bio-signal channels from the identical epoch and neighboring epochs can be obtained through two adaptive adjacency matrices learning methods. A jumping knowledge spatial-temporal graph convolution module helps the JK-STGCN 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. Experimental results on the ISRUC-S3 dataset showed that the overall accuracy achieved 0.831 and the F1-score and Cohen kappa reached 0.814 and 0.782, respectively, which are the competitive classification performance with the state-of-the-art baselines. Further experiments on the ISRUC-S3 dataset are also conducted to evaluate the execution efficiency of the JK-STGCN model. The training time on 10 subjects is 2621s and the testing time on 50 subjects is 6.8s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and U-Net architecture algorithms. Experimental results on the ISRUC-S1 dataset also demonstrate its generality, whose accuracy, F1-score, and Cohen kappa achieve 0.820, 0.798, and 0.767 respectively.A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) are utilized to classify sleep stages, after extracting features by a standard convolutional neural network (CNN) named FeatureNet. Intrinsic connections among different bio-signal channels from the identical epoch and neighboring epochs can be obtained through two adaptive adjacency matrices learning methods. A jumping knowledge spatial-temporal graph convolution module helps the JK-STGCN 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. Experimental results on the ISRUC-S3 dataset showed that the overall accuracy achieved 0.831 and the F1-score and Cohen kappa reached 0.814 and 0.782, respectively, which are the competitive classification performance with the state-of-the-art baselines. Further experiments on the ISRUC-S3 dataset are also conducted to evaluate the execution efficiency of the JK-STGCN model. The training time on 10 subjects is 2621s and the testing time on 50 subjects is 6.8s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and U-Net architecture algorithms. Experimental results on the ISRUC-S1 dataset also demonstrate its generality, whose accuracy, F1-score, and Cohen kappa achieve 0.820, 0.798, and 0.767 respectively. A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) are utilized to classify sleep stages, after extracting features by a standard convolutional neural network (CNN) named FeatureNet. Intrinsic connections among different bio-signal channels from the identical epoch and neighboring epochs can be obtained through two adaptive adjacency matrices learning methods. A jumping knowledge spatial-temporal graph convolution module helps the JK-STGCN 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. Experimental results on the ISRUC-S3 dataset showed that the overall accuracy achieved 0.831 and the F1-score and Cohen kappa reached 0.814 and 0.782, respectively, which are the competitive classification performance with the state-of-the-art baselines. Further experiments on the ISRUC-S3 dataset are also conducted to evaluate the execution efficiency of the JK-STGCN model. The training time on 10 subjects is 2621s and the testing time on 50 subjects is 6.8s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and U-Net architecture algorithms. Experimental results on the ISRUC-S1 dataset also demonstrate its generality, whose accuracy, F1-score, and Cohen kappa achieve 0.820, 0.798, and 0.767 respectively. |
| Author | Ji, Xiaopeng Wen, Peng Li, Yan |
| Author_xml | – sequence: 1 givenname: Xiaopeng orcidid: 0000-0002-4453-3541 surname: Ji fullname: Ji, Xiaopeng email: xiaopeng.ji@usq.edu.au organization: School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia – sequence: 2 givenname: Yan orcidid: 0000-0002-4694-4926 surname: Li fullname: Li, Yan email: yan.li@usq.edu.au organization: School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia – sequence: 3 givenname: Peng surname: Wen fullname: Wen, Peng email: paul.wen@usq.edu.au organization: School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, QLD, Australia |
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| Cites_doi | 10.1093/sleep/zsz306 10.1109/EMBC44109.2020.9176741 10.1504/IJBRA.2013.052447 10.1016/j.cmpb.2015.10.013 10.1007/978-3-642-35139-6_18 10.1109/CVPR.2016.90 10.1109/ICTAI.2017.00025 10.1007/s10439-015-1444-y 10.1016/j.eswa.2020.114031 10.1088/0967-3334/35/1/R1 10.1176/appi.ajp.2008.07121882 10.3389/fncom.2018.00085 10.1007/s40708-014-0003-x 10.1016/j.procs.2017.10.042 10.1016/j.cmpb.2019.105116 10.1001/archpsyc.1969.01740140118016 10.1109/TAFFC.2018.2817622 10.1109/TNSRE.2017.2776149 10.1109/CVPR.2015.7298594 10.1109/TNSRE.2021.3110665 10.24963/ijcai.2020/184 10.1164/rccm.2109080 10.1109/TAFFC.2020.2994159 10.24963/ijcai.2021/360 10.1038/s41746-021-00440-5 10.18653/v1/P17-1172 10.3389/fninf.2019.00045 10.1109/CVPR.2017.11 10.1016/j.cmpb.2011.11.005 10.1109/TIM.2018.2799059 10.1007/978-3-642-02962-2_47 10.1109/TNSRE.2016.2552539 10.1007/s00521-017-2919-6 10.1016/j.bspc.2007.05.005 10.1109/78.650093 10.1016/j.compbiomed.2019.01.013 10.1109/EMBC.2016.7591789 10.1016/j.bspc.2017.12.001 10.1609/aaai.v33i01.3301922 10.1109/TNSRE.2017.2721116 |
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| SubjectTerms | Aggregates Algorithms Artificial neural networks Classification Classification algorithms Convolution Datasets Deep learning EEG EKG Electrocardiography Electroencephalography Electromyography Feature extraction graph convolutional networks Jumping Manganese Neural networks Sleep sleep stage classification Temporal variations Testing time |
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| Title | Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification |
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