{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:18:57Z","timestamp":1772767137874,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009546","name":"North China University of Technology","doi-asserted-by":"publisher","award":["107051360022XN735"],"award-info":[{"award-number":["107051360022XN735"]}],"id":[{"id":"10.13039\/501100009546","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Sleep quality is related to people\u2019s physical and mental health, so an accurate assessment of sleep quality is key to recognizing sleep disorders and taking effective interventions. To address the shortcomings of traditional manual and automatic staging methods, such as being time-consuming and having low classification accuracy, an automatic sleep staging method based on the null space pursuit (NSP) decomposition algorithm of single-channel electroencephalographic (EEG) signals is proposed, which provides a new way for EEG signal decomposition and automatic identification of sleep stages. First, the single-channel EEG signal data from the Sleep-EDF database, DREAMS Subject database, and Sleep Heart Health Study database (SHHS), available on PhysioNet, were preprocessed, respectively. Second, the preprocessed single-channel EEG signals were decomposed by the NSP algorithm. Third, we extracted nine features in the time domain of the nonlinear dynamics and statistics from the original EEG signal and the six simple signals that were decomposed. Finally, the extreme gradient boosting (XGBOOST) algorithm was used to construct a classification model to classify and identify the 63 extracted EEG signal features for automatic sleep staging. The experimental results showed that, on the Sleep-EDF database, the accuracy of four and five categories were 93.59% and 92.89%, respectively; on the DREAMS Subject database, the accuracy rates of four and five categories were 91.32% and 90.01%, respectively; on the SHHS database, the accuracy rates of four and five categories were 90.25% and 88.37%, respectively. The experimental results show that the automatic sleep staging model proposed in this work has high classification accuracy and efficiency, as well as strong applicability and robustness.<\/jats:p>","DOI":"10.3390\/axioms12010030","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:38:43Z","timestamp":1672205923000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Sleep Staging Based on Single-Channel EEG Signal Using Null Space Pursuit Decomposition Algorithm"],"prefix":"10.3390","volume":"12","author":[{"given":"Weiwei","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Science, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongqian","family":"Linghu","sequence":"additional","affiliation":[{"name":"School of Science, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Science, North China University of Technology, Beijing 100144, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengzhen","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Science, China Pharmaceutical University, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Younes, M. (2017). The case for using digital EEG analysis in clinical sleep medicine. Sleep Sci. Pract., 1.","DOI":"10.1186\/s41606-016-0005-0"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.cmpb.2019.04.004","article-title":"Comparative analysis of different characteristics of automatic sleep stages","volume":"175","author":"Zhao","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_3","first-page":"101","article-title":"Evaluation of an automated single-channel sleep staging algorithm","volume":"7","author":"Wang","year":"2015","journal-title":"Nat. Sci. Sleep"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1001\/archpsyc.1969.01740140118016","article-title":"A manual of standardized terminology, Techniques and scoring system for sleep stages of human subjects","volume":"20","author":"Wolpert","year":"1969","journal-title":"Arch. Gen. Psychiatry."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"051908","DOI":"10.1103\/PhysRevE.65.051908","article-title":"Characterization of sleep stages by correlations in the magnitude and sign of heartbeat increments","volume":"65","author":"Kantelhardt","year":"2002","journal-title":"Phys. Rev. E"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1111\/j.1365-2869.2011.00981.x","article-title":"A new quantitative automatic method for the measurement of non-rapid eye movement sleep electroencephalographic amplitude variability","volume":"21","author":"Ferri","year":"2012","journal-title":"J. Sleep Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.jsmc.2020.05.005","article-title":"Overview of Telemedicine and Sleep Disorders","volume":"15","author":"Singh","year":"2020","journal-title":"Sleep Med. Clin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/TIM.2018.2799059","article-title":"Ensemble SVM Method for Automatic Sleep Stage Classification","volume":"67","author":"Alickovic","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.5665\/sleep.5046","article-title":"Computer-assisted automated scoring of polysomnograms using the somnolyzer system","volume":"38","author":"Punjabi","year":"2015","journal-title":"Sleep"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/j.compbiomed.2012.09.012","article-title":"An ensemble system for automatic sleep stage classification using single channel EEG signal","volume":"42","author":"Koley","year":"2012","journal-title":"Comput. Biol. Med."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"125685","DOI":"10.1016\/j.physa.2020.125685","article-title":"Automatic sleep staging with a single-channel EEG based on ensemble empirical mode decomposition","volume":"567","author":"Liu","year":"2021","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1109\/TSP.2010.2041606","article-title":"Null Space Pursuit: An Operator-based Approach to Adaptive Signal Separation","volume":"58","author":"Peng","year":"2010","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","unstructured":"(2021, October 15). The Sleep-EDF Database. Available online: https:\/\/physionet.org\/content\/sleep-edfx\/1.0.0\/."},{"key":"ref_16","unstructured":"(2021, November 13). The SHHS Database. Available online: https:\/\/physionet.org\/content\/shhpsgdb\/1.0.0\/."},{"key":"ref_17","unstructured":"(2021, November 11). The DREAMS Subjects Database. Available online: https:\/\/rdrr.io\/github\/boupetch\/rmdf\/man\/download_dreams_subjects.html."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.eswa.2018.03.020","article-title":"A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal","volume":"104","author":"Seifpour","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_20","first-page":"1077","article-title":"The sleep heart health study: Design, rationale and methods","volume":"20","author":"Quan","year":"1997","journal-title":"Sleep"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1093\/sleep\/21.7.759","article-title":"Methods for obtaining and analyzing unattended polysomnography data for a multicenter study","volume":"21","author":"Redline","year":"1998","journal-title":"Sleep"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3401","DOI":"10.1109\/78.806084","article-title":"Two denoising methods by wavelet transform","volume":"47","author":"Pan","year":"1999","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"577","DOI":"10.3390\/signals3030035","article-title":"A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform","volume":"3","author":"Grobbelaar","year":"2022","journal-title":"Signals"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.bspc.2014.03.007","article-title":"Classification of seizure based on the time frequency image of EEG signals using HHT and SVM","volume":"13","author":"Fu","year":"2014","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/1-4020-4995-1_7","article-title":"Features for audio classification: Percussiveness of sounds","volume":"7","author":"Skowronek","year":"2006","journal-title":"Intell. Algorithms Ambient. Biomed. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: A natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.nicl.2013.03.007","article-title":"Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis","volume":"2","author":"Kim","year":"2013","journal-title":"Neuroimage Clin."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.clinph.2009.10.033","article-title":"Seizure lateralization in scalp EEG using Hjorth parameters","volume":"121","author":"Cecchin","year":"2010","journal-title":"Clin. Neurophysiol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"International Biometric Society."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Koprinska, I. (2010, January 27\u201330). Feature Selection for Brain-Computer Interfaces. Proceedings of the Pacific-Asia conference on Knowledge of Discovery and Data Mining, New Frontiers in Applied Data Mining, Bangkok, Thailand.","DOI":"10.1007\/978-3-642-14640-4_8"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S1665-6423(13)71524-4","article-title":"Effectiveness of Wavelet Denoising on Electroencephalogram Signals","volume":"11","author":"Mamun","year":"2013","journal-title":"J. Appl. Res. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Phadikar, S., Sinha, N., Ghosh, R., and Ghaderpour, E. (2022). Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter. Sensors, 22.","DOI":"10.3390\/s22082948"}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/1\/30\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:52:19Z","timestamp":1760147539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/12\/1\/30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,27]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["axioms12010030"],"URL":"https:\/\/doi.org\/10.3390\/axioms12010030","relation":{},"ISSN":["2075-1680"],"issn-type":[{"value":"2075-1680","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,27]]}}}