Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep.
This study aims to develop an automatic detector of the A phases of the cyclic alternating pattern, periodic activity that generally occurs during non-REM (NREM) sleep. Eight polysomnographic recordings from healthy subjects were examined. From EEG recordings, five band descriptors, an activity desc...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 4; pp. 359 - 373 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2012
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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=104545218&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104545218 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2012 vid: 50 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104545218 NLM22430617 2011504891 10.1007/s11517-012-0881-0 NLM22430617 104545218 ppf: 359 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep. aug: au: Mariani S Manfredini E Rosso V Grassi A Mendez MO Alba A Matteucci M Parrino L Terzano MG Cerutti S Bianchi AM Mariani, Sara Manfredini, Elena Rosso, Valentina Grassi, Andrea Mendez, Martin O Alba, Alfonso Matteucci, Matteo Parrino, Liborio Terzano, Mario G affil: Department of Biomedical Engineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, 20133 Milan, Italy sug: subj: Signal Processing, Computer Assisted Sleep Stages Physiology Adult Algorithms Electroencephalography Methods Female Human Male Neural Networks (Computer) Polysomnography Methods Adult: 19-44 years Female Male ab: This study aims to develop an automatic detector of the A phases of the cyclic alternating pattern, periodic activity that generally occurs during non-REM (NREM) sleep. Eight polysomnographic recordings from healthy subjects were examined. From EEG recordings, five band descriptors, an activity descriptor and a variance descriptor were extracted and used to train different machine-learning algorithms. A visual scoring provided by an expert clinician was used as golden standard. Four alternative mathematical machine-learning techniques were implemented: (1) discriminant classifier, (2) support vector machines, (3) adaptive boosting, and (4) supervised artificial neural network. The results of the classification, compared with the visual analysis, showed average accuracies equal to 84.9 and 81.5% for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%. The SVM leads to accuracy of 81.9%. The performance achieved by the automatic classification is encouraging, since an efficient automatic classifier would benefit the practice in everyday clinics, preventing the physician from the time-consuming activity of the visually scoring of the sleep microstructure over whole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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