{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T14:57:53Z","timestamp":1786201073700,"version":"build-2736575974"},"reference-count":14,"publisher":"Institution of Engineering and Technology (IET)","issue":"3","license":[{"start":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T00:00:00Z","timestamp":1559520000000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Healthcare Tech Letters"],"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:p>\n                    Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter\u2010band energy ratio features obtained from multi\u2010band EEG signals for subject\u2010specific classification of sleep apnea and non\u2010apnea events. The\n                    <jats:italic>K<\/jats:italic>\n                    \u2010nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non\u2010apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.\n                  <\/jats:p>","DOI":"10.1049\/htl.2018.5101","type":"journal-article","created":{"date-parts":[[2019,4,24]],"date-time":"2019-04-24T22:19:52Z","timestamp":1556144392000},"page":"82-86","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Automatic detection of sleep apnea events based on inter\u2010band energy ratio obtained from multi\u2010band EEG signal"],"prefix":"10.1049","volume":"6","author":[{"given":"Suvasish","family":"Saha","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnab","family":"Bhattacharjee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaikh Anowarul","family":"Fattah","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2019,6,3]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJM200005113421901"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1164\/ajrccm.163.1.2001008"},{"key":"e_1_2_6_4_1","volume-title":"Sleep disorders medicine: basic science, technical considerations, and clinical aspects","author":"Chokroverty S.","year":"2013"},{"key":"e_1_2_6_5_1","volume-title":"Principles of neural science","author":"Kandel E.R.","year":"2000"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704\u2010012\u20102872\u20106"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10877\u2010015\u20109664\u20100"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.10.022"},{"key":"e_1_2_6_9_1","doi-asserted-by":"crossref","unstructured":"Almuhammadi W.S. Aboalayon K.A. Faezipour M.: \u2018Efficient obstructive sleep apnea classification based on EEG signals\u2019.IEEE Long Island Systems Applications and Technology Conf. (LISAT) Farmingdale New York May2015 pp.1\u20136 doi:10.1109\/LISAT.2015.7160186","DOI":"10.1109\/LISAT.2015.7160186"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.11.019"},{"key":"e_1_2_6_11_1","doi-asserted-by":"crossref","unstructured":"Azim M.R. Haque S.A. Amin M.S. et al. : \u2018Analysis of EEG and EMG signals for detection of sleep disordered breathing events\u2019.IEEE Int. Conf. Electrical and Computer Engineering (ICECE) Dhaka Bangladesh December2010 pp.646\u2013649 doi:10.1109\/ICELCE.2010.5700776","DOI":"10.1109\/ICELCE.2010.5700776"},{"key":"e_1_2_6_12_1","doi-asserted-by":"crossref","unstructured":"Saha S. Bhattacharjee A. Ansary M.A.A. et al. : \u2018An approach for automatic sleep apnea detection based on entropy of multi\u2010band EEG signal\u2019.IEEE Region 10 Conf. (TENCON) Singapore November2016 pp.420\u2013423 doi:10.1109\/TENCON.2016.7848033","DOI":"10.1109\/TENCON.2016.7848033"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2845303"},{"key":"e_1_2_6_14_1","unstructured":"PhysioNet: \u2018St. Vincent's university hospital\/university college Dublin sleep apnea database\u2019. Available athttp:\/\/physionet.org\/physiobank\/database\/ucddb accessed 1st October 2017"},{"key":"e_1_2_6_15_1","unstructured":"Greene J.: \u2018Feature subset selection using thorntons separability index and its applicability to a number of sparse proximity\u2010based classifiers\u2019.Proc. Annual Symp. Pattern Recognition Association SA 2001"}],"container-title":["Healthcare Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl.2018.5101","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/htl.2018.5101","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/htl.2018.5101","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T07:13:01Z","timestamp":1761635581000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/htl.2018.5101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6]]},"references-count":14,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["10.1049\/htl.2018.5101"],"URL":"https:\/\/doi.org\/10.1049\/htl.2018.5101","archive":["Portico"],"relation":{},"ISSN":["2053-3713","2053-3713"],"issn-type":[{"value":"2053-3713","type":"print"},{"value":"2053-3713","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6]]},"assertion":[{"value":"2018-11-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-04-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-06-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}