Investigation of an automatic sleep stage classification by means of multiscorer hypnogram.
Objectives: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the pos...
| Publicado en: | Methods of Information in Medicine Vol. 49; no. 5; pp. 467 - 473 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Thieme Medical Publishing Inc.
2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104930640&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104930640 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00261270 W7M jtl: Methods of Information in Medicine issn: 00261270 maglogo: N pubinfo: dt: 2010 vid: 49 iid: 5 pid: 2811 pub: Thieme Medical Publishing Inc. place: New York, New York artinfo: ui: 104930640 104930640 NLM20644896 2010829532 10.3414/ME09-02-0052 NLM20644896 104930640 ppf: 467 ppct: 6 formats: tig: atl: Investigation of an automatic sleep stage classification by means of multiscorer hypnogram. aug: au: Figueroa Helland VC Gapelyuk A Suhrbier A Riedl M Penzel T Kurths J Wessel N Figueroa Helland, V C Gapelyuk, A Suhrbier, A Riedl, M Penzel, T Kurths, J Wessel, N affil: Interdisciplinary Center for Dynamics of Complex Systems, University of Potsdam, Potsdam, Germany sug: subj: Polysomnography Methods Sleep Stages Algorithms Discriminant Analysis Electroencephalography Electromyography Human Reference Values Reproducibility of Results Respiratory Rate ab: Objectives: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers.Methods: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and only on epochs where the three experts agree in their sleep stage scoring.Results: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%.Conclusions: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm's assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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