{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T03:03:02Z","timestamp":1789614182497,"version":"build-2803163510"},"reference-count":42,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2017,6,2]],"date-time":"2017-06-02T00:00:00Z","timestamp":1496361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2017,6,2]],"date-time":"2017-06-02T00:00:00Z","timestamp":1496361600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2017,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA\u2009\u2009\u2a7e36 weeks) using multi-feature classification on a single EEG channel.\n                    <jats:italic>Approach<\/jats:italic>\n                    . Five EEG burst detectors relying on different machine learning approaches were compared: logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36\u201341 weeks PMA.\n                    <jats:italic>Main results<\/jats:italic>\n                    . The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements, LR provided the highest scores (Cohen\u2019s kappa\u2009\u2009=\u2009\u20090.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants\u2009\u2009\u2a7e36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights.\n                    <jats:italic>Significance<\/jats:italic>\n                    . In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/aa714a","type":"journal-article","created":{"date-parts":[[2017,5,5]],"date-time":"2017-05-05T06:15:19Z","timestamp":1493964919000},"page":"046015","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-feature classifiers for burst detection in single EEG channels from preterm infants"],"prefix":"10.1088","volume":"14","author":[{"given":"X","family":"Navarro","sequence":"first","affiliation":[{"name":"Sorbonne Universit\u00e9s"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F","family":"Por\u00e9e","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Rennes 1"},{"name":"INSERM"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M","family":"Kuchenbuch","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Rennes 1"},{"name":"INSERM"},{"name":"CHU Rennes"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M","family":"Chavez","sequence":"additional","affiliation":[{"name":"CNRS UMR-7225"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alain","family":"Beuch\u00e9e","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Rennes 1"},{"name":"INSERM"},{"name":"CHU Rennes"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G","family":"Carrault","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Rennes 1"},{"name":"INSERM"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2017,6,2]]},"reference":[{"key":"jneaa714abib001","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/0013-4694(89)90090-4","type":"journal-article","article-title":"Interburst interval measurements in the EEGs of premature infants with normal neurological outcome","volume":"73","author":"Hahn","year":"1989","journal-title":"Electroencephal. Clin. Neurophysiol."},{"key":"jneaa714abib002","doi-asserted-by":"publisher","first-page":"F163","DOI":"10.1136\/fn.84.3.F163","type":"journal-article","article-title":"Background electroencephalographic (EEG) activities of very preterm infants born at less than 27 weeks gestation: a study on the degree of continuity","volume":"84","author":"Hayakawa","year":"2001","journal-title":"Arch. Dis. Child. Fetal Neonatal Ed."},{"key":"jneaa714abib003","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1093\/sleep\/15.5.442","type":"journal-article","article-title":"Comparison of EEG sleep measures in healthy full-term and preterm infants at matched conceptional ages","volume":"15","author":"Scher","year":"1992","journal-title":"Sleep"},{"key":"jneaa714abib004","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.neucli.2010.02.002","type":"journal-article","article-title":"Electroencephalography in premature and full-term infants. Developmental features and glossary","volume":"40","author":"Andr\u00e9","year":"2010","journal-title":"Clin. Neurophysiol."},{"key":"jneaa714abib005","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/S0987-7053(99)80051-3","type":"journal-article","article-title":"Electroencephalography of the premature and term newborn. Maturational aspects and glossary","volume":"29","author":"Lamblin","year":"1999","journal-title":"Clin. Neurophysiol."},{"key":"jneaa714abib006","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s00381-005-1167-8","type":"journal-article","article-title":"Prognostic value of EEG performed at term age in preterm infants","volume":"22","author":"Rand\u00f2","year":"2006","journal-title":"Child\u2019s Nervous Syst."},{"key":"jneaa714abib007","doi-asserted-by":"publisher","first-page":"e891","DOI":"10.1542\/peds.2012-1115","type":"journal-article","article-title":"EEG for predicting early neurodevelopment in preterm infants: an observational cohort study","volume":"130","author":"Hayashi-Kurahashi","year":"2012","journal-title":"Pediatrics"},{"key":"jneaa714abib008","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1016\/S1388-2457(01)00641-1","type":"journal-article","article-title":"Automated detection of trac\u00e9 alternant during sleep in healthy full-term neonates using discrete wavelet transform","volume":"112","author":"Turnbull","year":"2001","journal-title":"Clin. Neurophysiol."},{"key":"jneaa714abib009","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1023\/A:1016393904439","type":"journal-article","article-title":"Automatic analysis and monitoring of burst suppression in anesthesia","volume":"17","author":"S\u00e4rkel\u00e4","year":"2002","journal-title":"J. Clin. Monit. Comput."},{"key":"jneaa714abib010","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.neulet.2005.05.030","type":"journal-article","article-title":"Relationship between slow-wave EEG bursts and heart rate changes in preterm infants","volume":"385","author":"Pfurtscheller","year":"2005","journal-title":"Neurosci. Lett."},{"key":"jneaa714abib011","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1088\/1741-2560\/5\/4\/005","type":"journal-article","article-title":"Classification of burst and suppression in the neonatal electroencephalogram","volume":"5","author":"L\u00f6fhede","year":"2008","journal-title":"J. Neural Eng."},{"key":"jneaa714abib012","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1016\/j.clinph.2010.02.010","type":"journal-article","article-title":"Detection of \u2018EEG bursts\u2019 in the early preterm EEG: visual versus automated detection","volume":"121","author":"Palmu","year":"2010","journal-title":"Clin. Neurophysiol."},{"key":"jneaa714abib013","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/7\/1\/016007","type":"journal-article","article-title":"Automatic classification of background EEG activity in healthy and sick neonates","volume":"7","author":"L\u00f6fhede","year":"2010","journal-title":"J. Neural Eng."},{"key":"jneaa714abib014","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1088\/0967-3334\/32\/10\/010","type":"journal-article","article-title":"Automatic burst detection for the EEG of the preterm infant","volume":"32","author":"Jennekens","year":"2011","journal-title":"Physiol. Meas."},{"key":"jneaa714abib015","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/EMBC.2012.6345860","type":"other","article-title":"Automated EEG inter-burst interval detection in neonates with mild to moderate postasphyxial encephalopathy","author":"Matic","year":"2012"},{"key":"jneaa714abib016","doi-asserted-by":"publisher","first-page":"105","DOI":"10.5220\/0004186401050111","type":"journal-article","article-title":"Automatic burst detection based on line length in the premature EEG","author":"Koolen","year":"2013","journal-title":"Biosignals"},{"key":"jneaa714abib017","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.compbiomed.2014.08.011","type":"journal-article","article-title":"Evaluation of automatic feature detection algorithms in EEG: application to interburst intervals","volume":"54","author":"Chauvet","year":"2014","journal-title":"Comput. Biol. Med."},{"key":"jneaa714abib018","doi-asserted-by":"publisher","first-page":"3288","DOI":"10.1109\/EMBC.2014.6944325","type":"other","article-title":"Assessing instantaneous energy in the EEG: a non-negative, frequency-weighted energy operator In","author":"O\u2019Toole","year":"2014"},{"key":"jneaa714abib019","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/11\/6\/066007","type":"journal-article","article-title":"Holistic approach for automated background EEG assessment in asphyxiated full-term infants","volume":"11","author":"Matic","year":"2014","journal-title":"J. Neural Eng."},{"key":"jneaa714abib020","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1016\/j.clinph.2014.11.024","type":"journal-article","article-title":"Automated analysis of multi-channel EEG in preterm infants","volume":"126","author":"Murphy","year":"2015","journal-title":"Clin. Neurophysiol."},{"key":"jneaa714abib021","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1016\/j.clinph.2015.11.008","type":"journal-article","article-title":"Automatic measurement of interburst interval in premature neonates using range EEG","volume":"127","author":"Navakatikyan","year":"2016","journal-title":"Clin. Neurophysiol."},{"key":"jneaa714abib022","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/S0733-8619(18)31025-9","type":"journal-article","article-title":"The electroencephalogram in altered states of consciousness","volume":"3","author":"Brenner","year":"1985","journal-title":"Neurol. Clin."},{"key":"jneaa714abib023","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/0013-4694(94)90065-5","type":"journal-article","article-title":"Background EEG activity in preterm infants: correlation of outcome with selected maturational features","volume":"91","author":"Biagioni","year":"1994","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"jneaa714abib024","doi-asserted-by":"publisher","first-page":"N85","DOI":"10.1088\/0967-3334\/31\/11\/N02","type":"journal-article","article-title":"Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG","volume":"31","author":"Palmu","year":"2010","journal-title":"Physiol. Meas."},{"key":"jneaa714abib025","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1109\/ICASSP.1990.115702","type":"other","article-title":"On a simple algorithm to calculate the \u2018energy\u2019 of a signal","volume":"1","author":"Kaiser","year":"1990"},{"key":"jneaa714abib026","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/ISCAS.1999.779976","type":"other","article-title":"Adaptive segmentation of electroencephalographic data using a nonlinear energy operator","volume":"4","author":"Agarwal","year":"1999"},{"key":"jneaa714abib027","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1159\/000351035","type":"journal-article","article-title":"Possible predictors of cardiorespiratory events after immunization in preterm neonates","volume":"104","author":"Mialet-Marty","year":"2013","journal-title":"Neonatology"},{"key":"jneaa714abib028","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0006-8993(74)90454-5","type":"journal-article","article-title":"The behavioural states of the newborn infant (a review)","volume":"76","author":"Prechtl","year":"1974","journal-title":"Brain Res."},{"key":"jneaa714abib029","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1016\/j.neuroimage.2006.11.004","type":"journal-article","article-title":"Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis","volume":"34","author":"Delorme","year":"2007","journal-title":"Neuroimage"},{"key":"jneaa714abib030","author":"Arce","year":"2005","type":"book"},{"key":"jneaa714abib031","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/978-1-4612-1694-0_15","type":"journal-article","article-title":"Information theory and an extension of the maximum likelihood principle","author":"Akaike","year":"1998","journal-title":"Selected Papers of Hirotugu Akaike"},{"key":"jneaa714abib032","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/S0740-0020(02)00104-1","type":"journal-article","article-title":"Comparison of logistic regression and neural network-based classifiers for bacterial growth","volume":"20","author":"Hajmeer","year":"2003","journal-title":"Food Microbiol."},{"key":"jneaa714abib033","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1093\/bioinformatics\/btm287","type":"journal-article","article-title":"Logistic regression for disease classification using microarray data: model selection in a large p and small n case","volume":"23","author":"Liao","year":"2007","journal-title":"Bioinformatics"},{"key":"jneaa714abib034","author":"Ripley","year":"2007","type":"book"},{"key":"jneaa714abib035","author":"Hosmer","year":"2004","type":"book"},{"key":"jneaa714abib036","volume":"vol 544","author":"McLachlan","year":"2004","type":"book"},{"key":"jneaa714abib037","author":"Sch\u00f6lkopf","year":"2002","type":"book"},{"key":"jneaa714abib038","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","type":"journal-article","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"jneaa714abib039","first-page":"1889","type":"journal-article","article-title":"Working set selection using second order information for training support vector machines","volume":"6","author":"Fan","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"jneaa714abib040","author":"Duda","year":"2012","type":"book"},{"key":"jneaa714abib041","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","type":"journal-article","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Edu. Psychol. Meas."},{"key":"jneaa714abib042","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.neucli.2007.10.008","type":"journal-article","article-title":"Normal EEG of premature infants born between 24 and 30 weeks gestational age: terminology, definitions and maturation aspects","volume":"37","author":"Vecchierini","year":"2007","journal-title":"Neurophysiol. Clin.\/Clin. Neurophysiol."}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"http:\/\/stacks.iop.org\/1741-2552\/14\/i=4\/a=046015?key=crossref.9b260cc14d297f3fdd084be604a69d01","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"http:\/\/stacks.iop.org\/1741-2552\/14\/i=4\/a=046015\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"http:\/\/stacks.iop.org\/1741-2552\/14\/i=4\/a=046015\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/stacks.iop.org\/1741-2552\/14\/i=4\/a=046015?key=crossref.9b260cc14d297f3fdd084be604a69d01","content-type":"text\/html","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T13:24:36Z","timestamp":1784985876000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/aa714a"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,2]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,6,2]]},"published-print":{"date-parts":[[2017,8,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/aa714a","relation":{},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,2]]},"assertion":[{"value":"Multi-feature classifiers for burst detection in single EEG channels from preterm infants","name":"article_title","label":"Article Title"},{"value":"Journal of Neural Engineering","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2017 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2017-02-10","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2017-05-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2017-06-02","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}