Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain.
The main objective of this study was to enhance the performance of sleep stage classification using single-channel electroencephalograms (EEGs), which are highly desirable for many emerging technologies, such as telemedicine and home care. The proposed method consists of decomposing EEGs by a discre...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 2; pp. 343 - 353 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2017
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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=121002485&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121002485 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2017 vid: 55 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121002485 121002485 NLM27193344 10.1007/s11517-016-1519-4 NLM27193344 121002485 ppf: 343 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain. aug: au: Silveira, Thiago Kozakevicius, Alice Rodrigues, Cesar da Silveira, Thiago L T Kozakevicius, Alice J Rodrigues, Cesar R affil: Graduate Program in Informatics , Federal University of Santa Maria , Santa Maria Brazil sug: subj: Signal Processing, Computer Assisted Electroencephalography Methods Sleep Stages Female Models, Statistical Adult Male Adult: 19-44 years Female Male ab: The main objective of this study was to enhance the performance of sleep stage classification using single-channel electroencephalograms (EEGs), which are highly desirable for many emerging technologies, such as telemedicine and home care. The proposed method consists of decomposing EEGs by a discrete wavelet transform and computing the kurtosis, skewness and variance of its coefficients at selected levels. A random forest predictor is trained to classify each epoch into one of the Rechtschaffen and Kales' stages. By performing a comprehensive set of tests on 106,376 epochs available from the Physionet public database, it is demonstrated that the use of these three statistical moments has enhanced performance when compared to their application in the time domain. Furthermore, the chosen set of features has the advantage of exhibiting a stable classification performance for all scoring systems, i.e., from 2- to 6-state sleep stages. The stability of the feature set is confirmed with ReliefF tests which show a performance reduction when any individual feature is removed, suggesting that this group of feature cannot be further reduced. The accuracies and kappa coefficients yield higher than 90 % and 0.8, respectively, for all of the 2- to 6-state sleep stage classification cases. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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