liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Ensemble SVM Method for Automatic Sleep Stage Classification
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
Effat Univ, Saudi Arabia.
2018 (engelsk)Inngår i: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 67, nr 6, s. 1258-1265Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Sleep scoring is used as a diagnostic technique in the diagnosis and treatment of sleep disorders. Automated sleep scoring is crucial, since the large volume of data should be analyzed visually by the sleep specialists which is burdensome, time-consuming tedious, subjective, and error prone. Therefore, automated sleep stage classification is a crucial step in sleep research and sleep disorder diagnosis. In this paper, a robust system, consisting of three modules, is proposed for automated classification of sleep stages from the single-channel electroencephalogram (EEG). In the first module, signals taken from Pz-Oz electrode were denoised using multiscale principal component analysis. In the second module, the most informative features are extracted using discrete wavelet transform (DWT), and then, statistical values of DWT subbands are calculated. In the third module, extracted features were fed into an ensemble classifier, which can be called as rotational support vector machine (RotSVM). The proposed classifier combines advantages of the principal component analysis and SVM to improve classification performances of the traditional SVM. The sensitivity and accuracy values across all subjects were 84.46% and 91.1%, respectively, for the five-stage sleep classification with Cohens kappa coefficient of 0.88. Obtained classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single-channel EEG, and can be used effectively in medical and home-care applications.

sted, utgiver, år, opplag, sider
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 67, nr 6, s. 1258-1265
Emneord [en]
Discrete wavelet transform (DWT); multiscale principal component analysis (MSPCA); rotational support vector machine (RotSVM); single-channel electroencephalogram (EEG); sleep stage classification
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-148087DOI: 10.1109/TIM.2018.2799059ISI: 000431903000001OAI: oai:DiVA.org:liu-148087DiVA, id: diva2:1211352
Tilgjengelig fra: 2018-05-30 Laget: 2018-05-30 Sist oppdatert: 2018-06-25

Open Access i DiVA

fulltext(1211 kB)393 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1211 kBChecksum SHA-512
88c68fffd37f4cbdf0fe3f0b61ae621a46889657d9aeb08431a49ccb8962faab090fca23497ee0fd33b05609536c9d580018750915bddfcdc36bc645268eb4f7
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Søk i DiVA

Av forfatter/redaktør
Alickovic, Emina
Av organisasjonen
I samme tidsskrift
IEEE Transactions on Instrumentation and Measurement

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 393 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 774 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf