{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T07:59:00Z","timestamp":1762761540782,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T00:00:00Z","timestamp":1552867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Basic Key Research Program of China","award":["2013CB329502"],"award-info":[{"award-number":["2013CB329502"]}]},{"name":"National Nature Science Foundation of China","award":["61633010"],"award-info":[{"award-number":["61633010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalography (EEG) signals may provide abundant information reflecting the developmental changes in brain status. It usually takes a long time to finally judge whether a brain is dead, so an effective pre-test of brain states method is needed. In this paper, we present a hybrid processing pipeline to differentiate brain death and coma patients based on canonical correlation analysis (CCA) of power spectral density, complexity features, and feature fusion for group analysis. In addition, time-varying power spectrum and complexity were observed based on the analysis of individual patients, which can be used to monitor the change of brain status over time. Results showed three major differences between brain death and coma groups of EEG signal: slowing, increased complexity, and the improvement on classification accuracy with feature fusion. To the best of our knowledge, this is the first scheme for joint general analysis and time-varying state monitoring. Delta-band relative power spectrum density and permutation entropy could effectively be regarded as potential features of discrimination analysis on brain death and coma patients.<\/jats:p>","DOI":"10.3390\/s19061342","type":"journal-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T12:18:53Z","timestamp":1552911533000},"page":"1342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8223-856X","authenticated-orcid":false,"given":"Li","family":"Zhu","sequence":"first","affiliation":[{"name":"Cognitive Science Department, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaochao","family":"Cui","sequence":"additional","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8560, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianting","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Information System, Saitama Institute of Technology, Fukaya, Saitama 369-0203, Japan"},{"name":"RIKEN Center for Advanced Intelligence Project, RIKEN, Nihonbashi, Tokyo 103-0027, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrzej","family":"Cichocki","sequence":"additional","affiliation":[{"name":"Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia"},{"name":"Department of Informatics, Nicolaus Copernicus University, 87-100 Torun, Poland"},{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5992-0405","authenticated-orcid":false,"given":"Jianhai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changle","family":"Zhou","sequence":"additional","affiliation":[{"name":"Cognitive Science Department, Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118","DOI":"10.14253\/acn.2017.19.2.118","article-title":"Electroencephalography for the diagnosis of brain death","volume":"19","author":"Lee","year":"2017","journal-title":"Ann. Clin. Neurophysiol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Quality Standards Subcommittee of the American Academy of Neurology (1995). Practice parameters for determining brain death in adults (summary statement). Neurology, 45, 1012\u20131014.","DOI":"10.1212\/WNL.45.5.1012"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11571-008-9047-z","article-title":"An empirical EEG analysis in brain death diagnosis for adults","volume":"2","author":"Chen","year":"2008","journal-title":"Cognit. Neurodyn."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1001\/archneur.1987.00520210048018","article-title":"Electroencephalographic activity after brain death","volume":"44","author":"Grigg","year":"1987","journal-title":"Arch. Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1159\/000117330","article-title":"Reliability of electroencephalogram in the diagnosis of brain death","volume":"30","author":"Buchner","year":"1990","journal-title":"Eur. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1212\/WNL.58.1.20","article-title":"Brain death worldwide: Accepted fact but no global consensus in diagnostic criteria","volume":"58","author":"Wijdicks","year":"2002","journal-title":"Neurology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1870","DOI":"10.1212\/WNL.0000000000001540","article-title":"Brain death declaration: Practices and perceptions worldwide","volume":"84","author":"Wahlster","year":"2015","journal-title":"Neurology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11571-014-9325-x","article-title":"Power spectral density and coherence analysis of Alzheimers EEG","volume":"9","author":"Wang","year":"2015","journal-title":"Cognit. Neurodyn."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tierney, A.L., Gabard-Durnam, L., Vogel-Farley, V., Tager-Flusberg, H., and Nelson, C.A. (2012). Developmental trajectories of resting EEG power: An endophenotype of autism spectrum disorder. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0039127"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1016\/j.clinph.2008.01.013","article-title":"EEG power and coherence in autistic spectrum disorder","volume":"119","author":"Coben","year":"2008","journal-title":"Clin. Neurophysiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1093\/bja\/aet409","article-title":"Age-related changes in EEG power spectra in infants during sevoflurane wash-out","volume":"112","author":"Sury","year":"2013","journal-title":"Br. J. Anaesth."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ijpsycho.2007.11.002","article-title":"Quantitative EEG in early Alzheimer\u2019s disease patients power spectrum and complexity features","volume":"68","author":"Czigler","year":"2008","journal-title":"Int. J. Psychophysiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1159\/000464418","article-title":"Power spectral density analysis of electrocorticogram recordings during cerebral hypothermia in neonatal seizures","volume":"24","author":"Myers","year":"2017","journal-title":"Ann. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2008\/462593","article-title":"Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy","volume":"2008","author":"Sakkalis","year":"2008","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.compbiomed.2004.11.001","article-title":"Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection","volume":"36","author":"Subasi","year":"2006","journal-title":"Comput. Biol. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.rbmret.2007.11.003","article-title":"Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques","volume":"29","author":"Faust","year":"2008","journal-title":"IRBM"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1007\/s00521-016-2594-z","article-title":"An entropy fusion method for feature extraction of EEG","volume":"29","author":"Chen","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.chaos.2015.09.002","article-title":"Complexity testing techniques for time series data: A comprehensive literature review","volume":"81","author":"Tang","year":"2015","journal-title":"Chaos Solitons Fractals"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"645","DOI":"10.3389\/fnins.2018.00645","article-title":"Do complexity measures of frontal EEG distinguish loss of consciousness in geriatric patients under anesthesia?","volume":"12","author":"Eagleman","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3389\/fninf.2019.00004","article-title":"Abnormal entropy modulation of the EEG signal in patients with schizophrenia during the auditory paired-stimulus paradigm","volume":"13","author":"Xiang","year":"2019","journal-title":"Front. Neuroinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1550010","DOI":"10.1142\/S0129065715500100","article-title":"Classification of obsessive compulsive disorder by EEG complexity and hemispheric dependency measurements","volume":"25","author":"Aydin","year":"2015","journal-title":"Int. J. Neural Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.bspc.2011.07.007","article-title":"Automated diagnosis of epileptic EEG using entropies","volume":"7","author":"Acharya","year":"2012","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1088\/0967-3334\/27\/3\/003","article-title":"Entropy analysis of the EEG background activity in Alzheimer\u2019s disease patients","volume":"27","author":"Hornero","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5668","DOI":"10.3390\/e16115668","article-title":"Permutation entropy applied to the characterization of the clinical evolution of epileptic patients under pharmacologicaltreatment","volume":"16","author":"Mateos","year":"2014","journal-title":"Entropy"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3389\/fncom.2015.00016","article-title":"EEG entropy measures in anesthesia","volume":"9","author":"Liang","year":"2015","journal-title":"Front. Comput. Neurosci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shiman, F., Safavi, S., Vaneghi, F., Oladazimi, M., Safari, M., and Ibrahim, F. (2012, January 5\u20137). EEG feature extraction using parametric and non-parametric models. Proceedings of the 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hong Kong, China.","DOI":"10.1109\/BHI.2012.6211507"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chai, X., Weng, X., Zhang, Z., Lu, Y., Liu, G., and Niu, H. (2019). Quantitative EEG in mild cognitive impairment and Alzheimers Disease by AR-spectral and multi-scale entropy analysis. World Congress on Medical Physics and Biomedical Engineering 2018, Springer.","DOI":"10.1007\/978-981-10-9038-7_29"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"013110","DOI":"10.1063\/1.4906038","article-title":"Multiple feature extraction and classification of electroencephalograph signal for Alzheimers\u2019 with spectrum and bispectrum","volume":"25","author":"Wang","year":"2015","journal-title":"Chaos Interdiscip. J. Nonlinear Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.eswa.2011.07.064","article-title":"EEG based automated detection of auditory loss: A pilot study","volume":"39","author":"Sriraam","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MSP.2007.4286561","article-title":"Jackknifing multitaper spectrum estimates","volume":"24","author":"Thomson","year":"2007","journal-title":"IEEE Signal Proc. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liang, Z., Duan, X., and Li, X. (2016). Entropy measures in neural signals. Signal Processing in Neuroscience, Springer.","DOI":"10.1007\/978-981-10-1822-0_8"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9072","DOI":"10.1016\/j.eswa.2012.02.040","article-title":"Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework","volume":"39","author":"Acharya","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.eswa.2015.10.047","article-title":"Fully automatic face normalization and single sample face recognition in unconstrained environments","volume":"47","author":"Haghighat","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.neucom.2016.09.057","article-title":"Assessment of driving fatigue based on intra\/inter-region phase synchronization","volume":"219","author":"Wanzeng","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_36","first-page":"246","article-title":"To Bonferroni or not to Bonferroni: When and how are the questions","volume":"81","author":"Cabin","year":"2000","journal-title":"Bull. Ecol. Soc. Am."},{"key":"ref_37","first-page":"27","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/clinchem\/39.4.561","article-title":"Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine","volume":"39","author":"Zweig","year":"1993","journal-title":"Clin. Chem."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.neucom.2016.05.113","article-title":"Discriminative extreme learning machine with supervised sparsity preserving for image classification","volume":"261","author":"Peng","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Keller, K., Mangold, T., Stolz, I., and Werner, J. (2017). Permutation entropy: New ideas and challenges. Entropy, 19.","DOI":"10.20944\/preprints201702.0071.v1"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ni, L., Cao, J., and Wang, R. (2013). Analyzing EEG of quasi-brain-death based on dynamic sample entropy measures. Comput. Math. Methods Med.","DOI":"10.1155\/2013\/618743"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1080\/0952813X.2010.506289","article-title":"EEG data analysis based on EMD for coma and quasi-brain-death patients","volume":"23","author":"Shi","year":"2011","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yin, Y., Zhu, H., Tanaka, T., and Cao, J. (2012, January 21\u201325). Analyzing the EEG energy of healthy human, comatose patient and brain death using multivariate empirical mode decomposition algorithm. Proceedings of the 2012 IEEE 11th International Conference on Signal Processing, Beijing, China.","DOI":"10.1109\/ICoSP.2012.6491622"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7189","DOI":"10.24297\/ijct.v15i11.4377","article-title":"EEG analysis for differentiating between brain death and coma in humans","volume":"15","author":"Gaochao","year":"2016","journal-title":"Int. J. Comput. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Cui, G., Yin, Y., Tanaka, T., and Cao, J. (2014, January 6\u201311). Eeg energy analysis for evaluating consciousness level using dynamic memd. Proceedings of the IEEE 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889716"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/6\/1342\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:38:36Z","timestamp":1760186316000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/6\/1342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,18]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["s19061342"],"URL":"https:\/\/doi.org\/10.3390\/s19061342","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,3,18]]}}}