Document
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Medical and Biological Engineering and Computing
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publication venue for: Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep

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RDF description of Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep - http://orbis.uaslp.mx/vivo/individual/scopus3065
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date: 2026-08-27T18:17:33
publisher: vivo
type: Document

Article
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FP7-247777
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funding vehicle for: Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep

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Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep
type: Article
related by: authorship_17876
related by: authorship_17871
type: Document
page end: 372
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issue: 4
type: Information Content Entity
has publication venue: Medical and Biological Engineering and Computing
estado: published
e iscopus: 2-s2.0-84861453240
doi: 10.1007/s11517-012-0881-0
related by: authorship_17875
origen: scopus
related by: authorship_17873
related by: authorship_17879
accesstype: All Open Access, Green
related by: authorship_17878
type: Thing
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date time value: article date 3066
most specific type: Article
related by: authorship_17877
related by: authorship_17872
crisid: 3066
has funding vehicle: FP7-247777
freetext keyword: Cyclic alternating pattern; Machine learning; Neural networks; Sleep; Support vector machines Automatic classification; Automatic classifiers; Automatic detector; Cyclic alternating pattern; Descriptors; Discriminant classifiers; EEG recording; Healthy subjects; Linear discriminants; Machine learning techniques; Objective criteria; Periodic activity; Sleep; Sleep recordings; Visual analysis; Adaptive boosting; Learning systems; Neural networks; Sleep research; Support vector machines; adult; algorithm; article; artificial neural network; electroencephalography; female; human; male; methodology; physiology; polysomnography; signal processing; sleep stage; support vector machine; Adult; Algorithms; Electroencephalography; Female; Humans; Male; Neural Networks (Computer); Polysomnography; Signal Processing, Computer-Assisted; Sleep Stages; Support Vector Machines
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page start: 359
pmid: 22430617
related by: authorship_17874
abstract: 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%25 for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%25. The SVM leads to accuracy of 81.9%25. 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 sleepmicrostructure overwhole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement. © International Federation for Medical and Biological Engineering 2012.
volume: 50
most specific type: Document