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dc.contributor.authorYan, Rui
dc.contributor.authorZhang, Chi
dc.contributor.authorSpruyt, Karen
dc.contributor.authorWei, Lai
dc.contributor.authorWang, Zhiqiang
dc.contributor.authorTian, Lili
dc.contributor.authorLi, Xueqiao
dc.contributor.authorRistaniemi, Tapani
dc.contributor.authorZhang, Jihui
dc.contributor.authorCong, Fengyu
dc.date.accessioned2019-01-21T08:16:32Z
dc.date.available2019-01-21T08:16:32Z
dc.date.issued2019
dc.identifier.citationYan, R., Zhang, C., Spruyt, K., Wei, L., Wang, Z., Tian, L., Li, X., Ristaniemi, T., Zhang, J., & Cong, F. (2019). Multi-modality of polysomnography signals’ fusion for automatic sleep scoring. <i>Biomedical Signal Processing and Control</i>, <i>49</i>, 14-23. <a href="https://doi.org/10.1016/j.bspc.2018.10.001" target="_blank">https://doi.org/10.1016/j.bspc.2018.10.001</a>
dc.identifier.otherCONVID_28752027
dc.identifier.otherTUTKAID_79696
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/62551
dc.description.abstractObjective: The study aims to develop an automatic sleep scoring method by fusing different polysomnography (PSG) signals and further to investigate PSG signals’ contribution to the scoring result. Methods: Eight combinations of four modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) were considered to find the optimal fusion of PSG signals. A total of 232 features, covering statistical characters, frequency characters, time-frequency characters, fractal characters, entropy characters and nonlinear characters, were derived from these PSG signals. To select the optimal features for each signal fusion, four widely used feature selection methods were compared. At the classification stage, five different classifiers were employed to evaluate the validity of the features and to classify sleep stages. Results: For the database in the present study, the best classifier, random forest, realized the optimal consistency of 86.24% with the sleep macrostructures scored by the technologists trained at the Sleep Center. The optimal accuracy was achieved by fusing four modalities of PSG signals. Specifically, the top twelve features in the optimal feature set were respectively EEG features named zero-crossings, spectral edge, relative power spectral of theta, Petrosian fractal dimension, approximate entropy, permutation entropy and spectral entropy, and EOG features named spectral edge, approximate entropy, permutation entropy and spectral entropy, and the mutual information between EEG and submental EMG. In addition, ECG features (e.g. Petrosianfractaldimension, zero-crossings,meanvalue ofRamplitude andpermutation entropy) were useful for the discrimination among W, S1 and R. Conclusions: Through exploring the different fusions of multi-modality signals, the present study concluded that the multi-modality of PSG signals’ fusion contributed to higher accuracy, and the optimal feature set was a fusion of multiple types of features. Besides, compared with manual scoring, the proposed automatic scoringmethods were cost-effective, which would alleviate the burden ofthe physicians, speed up sleep scoring, and expedite sleep research.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesBiomedical Signal Processing and Control
dc.rightsCC BY-NC-ND 4.0
dc.subject.otheruni (biologiset ilmiöt)
dc.subject.otherpolysomnography
dc.subject.othermulti-modality analysis
dc.subject.otherautomatic sleep scoring
dc.titleMulti-modality of polysomnography signals’ fusion for automatic sleep scoring
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201901181245
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosPsykologian laitosfi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.laitosDepartment of Psychologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiainePsykologiafi
dc.contributor.oppiaineMonitieteinen aivotutkimuskeskusfi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiainePsychologyen
dc.contributor.oppiaineCentre for Interdisciplinary Brain Researchen
dc.contributor.oppiaineSchool of Wellbeingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-01-18T13:15:15Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange14-23
dc.relation.issn1746-8094
dc.relation.numberinseries0
dc.relation.volume49
dc.type.versionpublishedVersion
dc.rights.copyright© 2018 The Author(s).
dc.rights.accesslevelopenAccessfi
dc.subject.ysoaivotutkimus
dc.subject.ysosignaalinkäsittely
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p23705
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.bspc.2018.10.001
dc.type.okmA1


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