{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T22:49:35Z","timestamp":1780958975645,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T00:00:00Z","timestamp":1597795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61674100"],"award-info":[{"award-number":["61674100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s and Kale\u2019s (R&amp;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.<\/jats:p>","DOI":"10.3390\/s20174677","type":"journal-article","created":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T09:22:31Z","timestamp":1597828951000},"page":"4677","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6908-9144","authenticated-orcid":false,"given":"Huaming","family":"Shen","sequence":"first","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Ran","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meihua","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Allon","family":"Guez","sequence":"additional","affiliation":[{"name":"Faculty of Biomedical Engineering, Drexel University, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2385-6963","authenticated-orcid":false,"given":"Ang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiying","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,19]]},"reference":[{"key":"ref_1","unstructured":"Rectschaffen, A., and Kales, A. (1968). A Manual of Standardized Terminology, Techniques and Scoring Systems for Sleep Stages of Human Subjects."},{"key":"ref_2","unstructured":"Iber, C., Ancoliisrael, S., Chesson, A., and Quan, S.F. (2007). The AASM Manual for The Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, American Academy of Sleep Medicine."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jneumeth.2016.07.012","article-title":"A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features","volume":"271","author":"Hassan","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.cmpb.2019.105116","article-title":"EEG sleep stages identification based on weighted undirected complex networks","volume":"184","author":"Diykh","year":"2020","journal-title":"Comput Methods Programs Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1109\/TBME.2017.2702123","article-title":"A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea","volume":"65","author":"Kang","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112790","DOI":"10.1016\/j.eswa.2019.07.007","article-title":"Sleep EEG Signal Analysis Based on Correlation Graph Similarity Coupled with an Ensemble Extreme Machine Learning Algorithm","volume":"138","author":"Abdulla","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"180320","DOI":"10.1016\/j.jneumeth.2019.108320","article-title":"An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov model","volume":"324","author":"Ghimatgar","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105367","DOI":"10.1016\/j.knosys.2019.105367","article-title":"Automatic sleep stage classification using optimize flexible analytic wavelet transform","volume":"192","author":"Taran","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1007\/s13369-019-04197-8","article-title":"Automated Detection of Sleep Stages Using Energy-Localized Orthogonal Wavelet Filter Banks","volume":"45","author":"Sharma","year":"2020","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.bbe.2015.11.001","article-title":"Automatic sleep scoring using statistical features in the EMD domain and ensemble methods","volume":"36","author":"Hassan","year":"2016","journal-title":"Biocybern Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.cmpb.2016.12.015","article-title":"Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting","volume":"140","author":"Hassan","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1007\/s00521-017-2919-6","article-title":"Automatic sleep stage classification based on iterative filtering of electroencephalogram signals","volume":"28","author":"Sharma","year":"2017","journal-title":"Neural. Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105089","DOI":"10.1016\/j.cmpb.2019.105089","article-title":"Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG","volume":"183","author":"Zhang","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s11325-019-02008-w","article-title":"Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data","volume":"24","author":"Zhang","year":"2020","journal-title":"Sleep Breath"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108312","DOI":"10.1016\/j.jneumeth.2019.108312","article-title":"Deep convolutional neural network for classification of sleep stages from single-channel EEG signals","volume":"324","author":"Mousavi","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_16","first-page":"2073","article-title":"Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea","volume":"27","author":"Korkalainen","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.compbiomed.2019.01.013","article-title":"Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals","volume":"106","author":"Michielli","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/10.867928","article-title":"Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG","volume":"47","author":"Kemp","year":"2000","journal-title":"IEEE. Trans. Biomed. Eng."},{"key":"ref_19","first-page":"e215","article-title":"PhysioBank, PhysioToolkit and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circ. Res."},{"key":"ref_20","first-page":"747325","article-title":"Cancelling ECG artifacts in EEG using a modified independent component analysis approach","volume":"1","author":"Stephanie","year":"2008","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.cmpb.2015.10.013","article-title":"ISRUC-Sleep: A comprehensive public dataset for sleep researchers","volume":"124","author":"Khalighi","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s11063-016-9530-1","article-title":"Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition","volume":"45","author":"Zhang","year":"2017","journal-title":"Neural. Process Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.ymssp.2012.06.004","article-title":"An approach based on wavelet packet decomposition and Hilbert\u2013Huang transform (WPD\u2013HHT) for spindle bearings condition monitoring","volume":"33","author":"Law","year":"2012","journal-title":"Mech. Syst. Signal Process"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7544","DOI":"10.1109\/TVT.2019.2925903","article-title":"Fault Diagnosis of Train Plug Door Based on a Hybrid Criterion for IMFs Selection and Fractional Wavelet Package Energy Entropy","volume":"68","author":"Cao","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","first-page":"55","article-title":"N4SID: Numerical Algorithms for State Space Subspace System Identification","volume":"Volume 26","author":"Van","year":"1993","journal-title":"Associated Technologies and Recent Developments, Proceedings of the 12th Triennal World Congress of the International Federation of Automatic Control, Sydney, Australia, 18\u201323 July 1993"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"125268","DOI":"10.1109\/ACCESS.2019.2939038","article-title":"An accurate sleep stage classification method based on state space model","volume":"7","author":"Shen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compbiomed.2018.04.025","article-title":"An accurate sleep stage classification system using a new class of optimally time-frequency localized three-band wavelet filter bank","volume":"98","author":"Sharma","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TIM.2012.2187242","article-title":"Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models","volume":"61","author":"Liang","year":"2012","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neucom.2012.11.003","article-title":"Automatic sleep stage recurrent neural classifier using energy features of EEG signals","volume":"104","author":"Hsu","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.knosys.2017.05.005","article-title":"A decision support system for automated identification of sleep stages from single-channel EEG signals","volume":"128","author":"Hassan","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1109\/JBHI.2014.2303991","article-title":"Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal","volume":"18","author":"Zhu","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.eswa.2018.12.023","article-title":"Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement","volume":"121","author":"Jiang","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.compbiomed.2018.08.022","article-title":"Sleep stage classification using single-channel EOG","volume":"10","author":"Rahman","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1109\/TNSRE.2017.2721116","article-title":"A model for automatic sleep stage scoring based on raw single-channel EEG","volume":"25","author":"Supratak","year":"2017","journal-title":"IEEE T. Neur. Sys. Reh."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4677\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:03:01Z","timestamp":1760176981000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4677"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,19]]},"references-count":35,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174677"],"URL":"https:\/\/doi.org\/10.3390\/s20174677","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,19]]}}}