Please use this identifier to cite or link to this item: https://doi.org/10.1145/1873951.1874154
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dc.titleAutomated sleep quality measurement using EEG signal - First step towards a domain specific music recommendation system
dc.contributor.authorZhao, W.
dc.contributor.authorWang, X.
dc.contributor.authorWang, Y.
dc.date.accessioned2013-07-04T08:14:28Z
dc.date.available2013-07-04T08:14:28Z
dc.date.issued2010
dc.identifier.citationZhao, W.,Wang, X.,Wang, Y. (2010). Automated sleep quality measurement using EEG signal - First step towards a domain specific music recommendation system. MM'10 - Proceedings of the ACM Multimedia 2010 International Conference : 1079-1082. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/1873951.1874154" target="_blank">https://doi.org/10.1145/1873951.1874154</a>
dc.identifier.isbn9781605589336
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40879
dc.description.abstractWith the rapid pace of modern life, millions of people suffer from sleep problems. Music therapy, as a non-medication approach to mitigating sleep problems, has attracted increasing attention recently. However the adaptability of music therapy is limited by the time consuming task of choosing suitable music for users. Inspired by this observation, we discuss the concept of a domain specific music recommendation system, which automatically recommends music for users according to their sleep quality. The proposed system requires multidisciplinary efforts including automated sleep quality measurement and content-based music similarity measure. As a first step, we focus on the automated sleep quality measurement in this paper. An EEG-based approach is proposed to measure user's sleep quality. The advantages of our approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than six sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing Electroencephalography (EEG) signal only, so the user experience is improved because he is attached with fewer sensors during sleep. We conduct experiments based on a standard data set. Our approach achieves high accuracy and shows promising potential for the music recommendation system. © 2010 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1873951.1874154
dc.sourceScopus
dc.subjectmusic recommendation
dc.subjectmusic therapy
dc.subjectsleep disorder
dc.subjectsleep quality analysis
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/1873951.1874154
dc.description.sourcetitleMM'10 - Proceedings of the ACM Multimedia 2010 International Conference
dc.description.page1079-1082
dc.identifier.isiutNOT_IN_WOS
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