An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features
The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Jg. 20; H. 17; S. 4677 |
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| Abstract | The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen’s and Kale’s (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods. |
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| AbstractList | The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen’s and Kale’s (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods. The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen's and Kale's (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen's and Kale's (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods. |
| Author | Guez, Allon Xu, Meihua Li, Ang Guo, Aiying Shen, Huaming Ran, Feng |
| AuthorAffiliation | 1 School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China; ranfeng@shu.edu.cn (F.R.); mhxu@shu.edu.cn (M.X.); shulivia@shu.edu.cn (A.L.); gayshh@shu.edu.cn (A.G.) 2 Faculty of Biomedical Engineering, Drexel University, Philadelphia, PA 19104, USA; guezal@drexel.edu |
| AuthorAffiliation_xml | – name: 1 School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China; ranfeng@shu.edu.cn (F.R.); mhxu@shu.edu.cn (M.X.); shulivia@shu.edu.cn (A.L.); gayshh@shu.edu.cn (A.G.) – name: 2 Faculty of Biomedical Engineering, Drexel University, Philadelphia, PA 19104, USA; guezal@drexel.edu |
| Author_xml | – sequence: 1 givenname: Huaming orcidid: 0000-0001-6908-9144 surname: Shen fullname: Shen, Huaming – sequence: 2 givenname: Feng surname: Ran fullname: Ran, Feng – sequence: 3 givenname: Meihua surname: Xu fullname: Xu, Meihua – sequence: 4 givenname: Allon surname: Guez fullname: Guez, Allon – sequence: 5 givenname: Ang orcidid: 0000-0003-2385-6963 surname: Li fullname: Li, Ang – sequence: 6 givenname: Aiying surname: Guo fullname: Guo, Aiying |
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| Cites_doi | 10.1016/j.cmpb.2019.105089 10.1007/s11063-016-9530-1 10.1007/s00521-017-2919-6 10.1016/j.knosys.2017.05.005 10.1016/j.jneumeth.2019.108312 10.1016/j.bbe.2015.11.001 10.1109/JBHI.2014.2303991 10.1109/TIM.2012.2187242 10.1109/TVT.2019.2925903 10.1016/j.neucom.2012.11.003 10.1016/j.jneumeth.2003.10.009 10.1016/j.ymssp.2012.06.004 10.1016/j.cmpb.2019.105116 10.1109/TBME.2017.2702123 10.1007/s11325-019-02008-w 10.1016/j.compbiomed.2019.01.013 10.1016/j.eswa.2018.12.023 10.1016/j.compbiomed.2018.08.022 10.1007/s13369-019-04197-8 10.1016/j.jneumeth.2016.07.012 10.1016/j.jneumeth.2019.108320 10.1109/TNSRE.2017.2721116 10.1016/j.compbiomed.2018.04.025 10.1016/j.cmpb.2016.12.015 10.1016/j.eswa.2019.07.007 10.1109/10.867928 10.1109/ACCESS.2019.2939038 10.1016/j.knosys.2019.105367 10.1016/j.cmpb.2015.10.013 |
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| SubjectTerms | Accuracy Algorithms Classification Datasets Decomposition EEG Methods Neural networks Sleep sleep stage state space model wavelet packet Wavelet transforms |
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| Title | An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features |
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