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dc.contributor.authorKhandoker, AH
dc.contributor.authorPalaniswami, M
dc.contributor.authorKarmakar, CK
dc.date.available2014-05-21T22:48:38Z
dc.date.issued2009-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000262429100006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationKhandoker, A. H., Palaniswami, M. & Karmakar, C. K. (2009). Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 13 (1), pp.37-48. https://doi.org/10.1109/TITB.2008.2004495.
dc.identifier.issn1089-7771
dc.identifier.urihttp://hdl.handle.net/11343/29269
dc.description.abstractObstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS - ) and subjects with OSAS (OSAS +), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.
dc.languageEnglish
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.subjectArtificial Intelligence and Image Processing
dc.titleSupport Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings
dc.typeJournal Article
dc.identifier.doi10.1109/TITB.2008.2004495
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentElectrical and Electronic Engineering
melbourne.source.titleIEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
melbourne.source.volume13
melbourne.source.issue1
melbourne.source.pages37-48
dc.description.pagestart37
melbourne.publicationid125382
melbourne.elementsid310422
melbourne.contributor.authorKHANDOKER, AHSAN
melbourne.contributor.authorPalaniswami, Marimuthu
melbourne.contributor.authorKarmakar, Chandan
dc.identifier.eissn1558-0032
melbourne.accessrightsThis item is currently not available from this repository


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