{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:49:31Z","timestamp":1760237371930,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T00:00:00Z","timestamp":1585872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["PZ00P3_179795"],"award-info":[{"award-number":["PZ00P3_179795"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.<\/jats:p>","DOI":"10.3390\/s20072024","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T03:58:39Z","timestamp":1586231919000},"page":"2024","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Save Muscle Information\u2013Unfiltered EEG Signal Helps Distinguish Sleep Stages"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7232-9401","authenticated-orcid":false,"given":"Gi-Ren","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Mathematics, National Chen-Kung University, Tainan 701, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caroline","family":"Lustenberger","sequence":"additional","affiliation":[{"name":"Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, ETH Zurich, 8092 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Lun","family":"Lo","sequence":"additional","affiliation":[{"name":"Department of Thoracic Medicine, Healthcare Center, Chang Gung Memorial Hospital, School of Medicine, Chang Gung University, New Taipei 33302, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1281-8718","authenticated-orcid":false,"given":"Wen-Te","family":"Liu","sequence":"additional","affiliation":[{"name":"Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City 110, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan-Chung","family":"Sheu","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, National Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0253-3156","authenticated-orcid":false,"given":"Hau-Tieng","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Department of Statistical Science, Duke University, 120 Science Dr. Durham, NC 27708, USA"},{"name":"Mathematics Division, National Center for Theoretical Sciences, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s40708-017-0074-6","article-title":"Removal of muscular artifacts in EEG signals: A comparison of linear decomposition methods","volume":"5","author":"Dowding","year":"2018","journal-title":"Brain Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1111\/j.1365-2869.1995.tb00165.x","article-title":"Age trends in the sleep EEG of healthy older men and women","volume":"4","author":"Larsen","year":"1995","journal-title":"J. Sleep Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/0013-4694(83)90137-2","article-title":"Muscle spike artifact minimization in EEGs by time-domain filtering","volume":"55","author":"Barlow","year":"1983","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_4","unstructured":"Berry, R.B., Brooks, R., Gamaldo, C.E., Harding, S.M., Marcus, C., and Vaughn, B.V. (2012). The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, American Academy of Sleep Medicine."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"597","DOI":"10.5664\/jcsm.2172","article-title":"Rules for scoring respiratory events in sleep: Update of the 2007 AASM manual for the scoring of sleep and associated events","volume":"8","author":"Berry","year":"2012","journal-title":"J. Clin. Sleep Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16824","DOI":"10.1038\/s41598-019-53115-3","article-title":"Accurate whole-night sleep monitoring with dry-contact ear-EEG","volume":"9","author":"Mikkelsen","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2019.2914425","article-title":"Configurable mobile system for autonomous high-quality sleep monitoring and closed-loop acoustic stimulation","volume":"3","author":"Ferster","year":"2019","journal-title":"IEEE Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/TBME.2019.2911423","article-title":"Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor","volume":"67","author":"Nakamura","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"16743","DOI":"10.1038\/srep16743","article-title":"Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear","volume":"5","author":"Debener","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yazicioglu, R.F., Van Hoof, C., and Puers, R. (2008). Biopotential Readout Circuits for Portable Acquisition Systems, Springer Science & Business Media, Springer.","DOI":"10.1007\/978-1-4020-9093-6"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10916-008-9224-y","article-title":"Time frequency based coherence analysis between EEG and EMG activities in fatigue duration","volume":"34","author":"Tuncel","year":"2010","journal-title":"J. Med. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s10548-009-0081-x","article-title":"Relation of gamma oscillations in scalp recordings to muscular activity","volume":"22","author":"Pope","year":"2009","journal-title":"Brain Topogr."},{"key":"ref_13","unstructured":"Cisotto, G., Michieli, U., and Badia, L. (2017). A coherence study on EEG and EMG signals. arXiv, preprint."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"791","DOI":"10.5664\/jcsm.6618","article-title":"The accuracy, night-to-night variability, and stability of frontopolar sleep electroencephalography biomarkers","volume":"13","author":"Levendowski","year":"2017","journal-title":"J. Clin. Sleep Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"431","DOI":"10.2147\/NSS.S189167","article-title":"comparison of eMg power during sleep from the submental and frontalis muscles","volume":"10","author":"Levendowski","year":"2018","journal-title":"Nat. Sci. Sleep"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"835","DOI":"10.5665\/sleep.1886","article-title":"Normative EMG values during REM sleep for the diagnosis of REM sleep behavior disorder","volume":"35","author":"Frauscher","year":"2012","journal-title":"Sleep"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kim, H., and Choi, S. (2018, January 3\u20136). Automatic Sleep Stage Classification Using EEG and EMG Signal. Proceedings of the 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), Prague, Czech Republic.","DOI":"10.1109\/ICUFN.2018.8436712"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1007\/s10439-015-1444-y","article-title":"Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders","volume":"44","author":"Tsinalis","year":"2016","journal-title":"Ann. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1109\/TNSRE.2017.2721116","article-title":"DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG","volume":"25","author":"Supratak","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vilamala, A., Madsen, K.H., and Hansen, L.K. (2017, January 25\u201328). Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring. Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan.","DOI":"10.1109\/MLSP.2017.8168133"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101576","DOI":"10.1016\/j.bspc.2019.101576","article-title":"Diffuse to fuse EEG spectra\u2013Intrinsic geometry of sleep dynamics for classification","volume":"55","author":"Liu","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.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":"Circulation"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1002\/cpa.21413","article-title":"Group invariant scattering","volume":"65","author":"Mallat","year":"2012","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4114","DOI":"10.1109\/TSP.2014.2326991","article-title":"Deep scattering spectrum","volume":"62","author":"Mallat","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1214\/14-AOS1276","article-title":"Intermittent process analysis with scattering moments","volume":"43","author":"Bruna","year":"2015","journal-title":"Ann. Stat."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1162\/0899766042321814","article-title":"Canonical correlation analysis: An overview with application to learning methods","volume":"16","author":"Hardoon","year":"2004","journal-title":"Neural Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1016\/j.acha.2017.12.006","article-title":"Latent common manifold learning with alternating diffusion: Analysis and applications","volume":"47","author":"Talmon","year":"2019","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Scholkopf, B., and Smola, A.J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Efron, B., and Tibshirani, R.J. (1994). An Introduction to the Bootstrap, CRC Press.","DOI":"10.1201\/9780429246593"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Good, P. (1994). Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses, Springer-Verlag Series in Statistics.","DOI":"10.1007\/978-1-4757-2346-5"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"141","DOI":"10.11613\/BM.2015.015","article-title":"Understanding bland altman analysis","volume":"25","author":"Giavarina","year":"2015","journal-title":"Biochem. Med. Biochem. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1007\/BF01618421","article-title":"An introduction to bispectral analysis for the electroencephalogram","volume":"10","author":"Sigl","year":"1994","journal-title":"J. Clin. Monit."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lo, Y.L., Lin, T.Y., Fang, Y.F., Wang, T.Y., Chen, H.C., Chou, C.L., Chung, F.T., Kuo, C.H., Feng, P.H., and Liu, C.Y. (2011). Feasibility of bispectral index-guided propofol infusion for flexible bronchoscopy sedation: A randomized controlled trial. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0027769"},{"key":"ref_35","first-page":"1","article-title":"Effects of neuromuscular blockade reversal on bispectral index and frontal electromyogram during steady-state desflurane anesthesia: A randomized trial","volume":"9","author":"Kim","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_36","unstructured":"Lawoyin, S. (2014). Novel Technologies for the Detection and Mitigation of Drowsy Driving. [PhD Thesis, Biomedical Engineering Department, Virginia Commonwealth University]."},{"key":"ref_37","first-page":"279","article-title":"Quantitative evaluation of muscle relaxation induced by Kundalini yoga with the help of EMG integrator","volume":"34","author":"Narayan","year":"1990","journal-title":"Indian J. Physiol. Pharm."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/2024\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:15:22Z","timestamp":1760174122000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/2024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,3]]},"references-count":37,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20072024"],"URL":"https:\/\/doi.org\/10.3390\/s20072024","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,4,3]]}}}